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18/09/2015 16/09/2018 Books, Education, Feature, Health, Life, Review, Society 3 Comments Ab OsterhausAfrican JournalistsAidsBarack ObamaBill GatesBodyCatalystCenter For Diseases ControlChildrenCIAComputerContaminationDavid HodgesDavis IckeDepopulationDr. Lorraine DayDr. Rebecca CarleyEbolaEpidemicESWIFlu VaccineGeneral HospitalGovernmentsHealthHypodermicInfluenzaMilitaryPatrick JordanPharmaceuticalPopulationPublicRockefeller FoundationRothschildSan FranciscoScientistVaccinationsVaccinesWestern JournalistsWorld Health Organization
Aids & Ebola: The Evil Activities Of The Military, Medical, Political, Pharmaceutical And Rothschild/Rockfeller's Front-Man Barack Obama
By Johan van Dongen and Joel Savage
"The best way to manufacture a new epidemic of a deadly disease is to vaccinate the people with contaminated vaccines." Said Johan van Dongen cynically the other day, during one of my meetings with him about all publications he wrote over Aids and Ebola.
It is a fact that contagious diseases spread relatively slow unless everyone agrees to have the disease injected directly into his or her body by vaccination. That is the way to "grow" an epidemic rapidly! And this is not only the conclusion of investigative journalist and writer Joel Savage and micro-surgeon Johan van Dongen but also from thousands of other scientists worldwide and among them Doctor Lorraine Day.
So, therefore, all of us are stating: "It's apparently necessary for governments, the military, and medical establishments and almost all amateur Western and African journalists, who don't dare to publish the truth, about the fact that the World Health Organization (WHO) is terrifying the public into accepting vaccinations!
But why would our or your government and the World Health Organization want to give us terrible diseases? Don't they want the population to be healthy? Unfortunately, the answer is a very big "No." A healthy population is not good for business, particularly it's not good for the Pharmaceutical companies and their extremely dangerous and exorbitantly over-priced drug medications.
A healthy population, specifically in African and underdeveloped countries continues to multiply by having children. That's abhorrent to the New World Order/One World/ One Government who wants to reduce the population of the world from it's present 6+ billion, to 500 million – which requires that 5 1/2 billion people be exterminated in one way or another. And this World Government is helped by the Rockefeller Foundation, the Rothschild and……, Bill Gates!
Depopulation and culling the masses and Lorraine Day
In September of 2007, David Hodges interviewed Dr. Lorraine Day about the evils of the present medical system. She is the former Orthopedic Chief of Staff at San Francisco General Hospital and wife of retired Congressman Bill Dannemeyer. Doctor Day dropped a bombshell at the conclusion of the interview when she unabashedly stated that the global elite wants to murder 95% of the population.
Dr. Lorraine Day on her 77th birthday with her twin granddaughters who will soon be 3.
Hodges rejected her statement as a gross exaggeration
In April of 2009, Hodges interviewed medical researcher-author, Patrick Jordan, and Dr. Rebecca Carley at the beginning of the swine flu outbreak. Both guests made similar claims about an intentional depopulation program by the global elite. Dr. Carley and Patrick Jordan pointed to the H1N1 flu vaccine claiming it would be the primary catalyst in depopulating the world and that the cure would be far worse than the disease.
Patrick Jordan's research uncovers a three-vaccine system that the elite have developed. The first inoculation turns off white blood cells (the immune system); the second inject viruses, and the third switches the immune system on again. In the middle period, viruses are expanding around the body, but the person doesn't feel sick, because the immune system is not fighting them. When the immune system kicks in again, it unleashes such an assault on the virus cocktail that it kills the body. This is known as a cytokine storm when the immune system is so overwhelmed that it sends too many antibodies at the same time to infected areas of the body and the body kills itself.
The World Health Organization and the ESWI of Ab Osterhaus have asked for live swine flu virus to be in the vaccine and it is quite possible that they will expand and increase the strength of the virus through the vaccine. The WHO website says: 'In view of the anticipated limited vaccine availability at global level and the potential need to protect against "drifted" strains of a virus, SAGE recommended that promoting production and use of vaccines such as those that are formulated with oil-in-water adjuvants and live attenuated influenza vaccines was important.
The WHO advisory board and Ab Osterhaus on vaccination policy include executives from Baxter and Novartis. Baxter International said it will be shipping swine flu vaccine worldwide by the end of July and the potential profits are fantastic, but it's not primarily about money. It's about a long-planned mass-culling of the human population and the microchipping of every man, woman, and child.
In 1997, a CIA scientist told David Icke that microchips developed in the secret government-military research projects were then small enough to be injected by hypodermic needles in vaccination programs. With nanotechnology, no one would know. Computer technology communicating with the chips has the potential to manipulate people mentally, emotionally and physically. This could be done en masse or individually through the chip's unique transmitter-receiver signal.
Rothschild/Rockefeller's front-man Barack Obama and his Orwellian demonstration program to improve immunization coverage
The bill says:
"Under this program, CDC will provide grants to states to improve immunization coverage of children, adolescents, and adults through the use of evidence-based interventions. States may use funds to implement interventions that are recommended by the Community Preventive Services Task Force, such as reminders or recalls for patients or providers, or home visits."
Interventions? Home visits? This is one step away from immunizing your children by force or having you arrested for refusing to comply, warns Icke. World Health Organisation 'recommendations' are binding on its nearly 200 member countries when a pandemic emergency is declared under the International Health Regulations Act of 2005 and WHO pandemic plan of April this year. Margaret Chan, the WHO Director-General, has already declared a swine flu pandemic when there wasn't one so these emergency powers would be activated. Chan (a lackey of the Rothschild-Rockefeller cabal) is going to be recommending compulsory vaccination. She's done it already.
The Rockefeller-dominated Centers for Disease Control (CDC) in the United States, has said that children who have never had flu 'shot' may need to be vaccinated four times in the Autumn – twice for seasonal flu and twice for swine flu. Other children will get three shots – note the three, given Patrick Jordan's information. Rothschild-Rockefeller's front-man, Barack Obama, has demanded that his 'health reform' bill is passed into law, just ahead of mass vaccination. The bill authorizes the respective authorities to act freely.
13/09/2015 20/08/2016 Books, Education, Feature, Health, History, Life, Non-fiction, Review, Society Adolf HitlerAidsAlois HitlerAnglo Persian CompanyBASFBritainBritishBuyer GroupChancellorDiseasesDutch IndiesEbolaFritz ter MeerGermanyHenri DeterdingHoechstHollandI G FarbenLord FisherLudwig ErhardMaria Anna SchiclgruberMesopotamiaMexicoOilPersiaPharmaceuticalRockefeller FoundationRomaniaRothschildRoyal Dutch-ShellRussiaStandard OilTelford TaylorTrinidadWorld War IWorld War II
Aids And Ebola: Consequences Of World War II, And The Financial Support Of Adolf Hitler By The Rockefeller Foundation
By Professor/Micro-Surgeon Johan Van Dongen
(The Rockefeller Foundation, a mafioso family in sheep's clothing, worked with Adolf Hitler to execute his wicked projects and plans.)
The pharmaceutical investment in business, associated with the cure of diseases, is obviously not a good combination or idea. Either the financial interests of a handful of pharmaceutical shareholders gain ground, or the health interests of seven billion people are at stake.
Diseases will continue to exist as long as there is a pharmaceutical industry, which aims and objectives are to make a profit from sick people. Thus; with the collaboration of bad scientists and laboratory assistance in the medical field, old and new diseases are created to continue generating profit for the pharmaceutical company. Thus; only through the abolition of the pharmaceutical business, will the diseases ceased to exist or reduced. But it's totally impossible to abolish pharmaceutical companies, the reason diseases are common.
The Rockefeller Foundation and the Mafioso Factory
Transactions and money earned in the IG Farben company were fully donated to the Nazi German Kaiser Wilhelm Institute for Psychiatry and the Kaiser Wilhelm Institute for Anthropology. Both institutes devoted themselves at that time entirely on research into eugenics with the aim of studying human genetics and susceptibility to specific diseases such as horse Aids (EIAV) and hemorrhagic fever (Ebola). The embryo for Aids and Ebola was born into a monstrosity that now causes death and destruction among world citizens and mostly Africans.
Even this was not enough because John D. Rockefeller appointed the Swiss fascist and scientist Ernst Rudin as the head of both institutes. In addition, John also paid the two assistants and proteges of Rudin, with the melodious names Otmar von Verschuer and Frans J. Kallman. Just three months after their appointment, Hitler came to power and whether he was paid by Rockefeller Foundation remains a mystery.
What we do know is that the Rockefellers' device was placed under Hitler's control, in the field of eugenics, a company that also dealt with the Acquired Immunodeficiency Virus Equine EIAV, known popularly as horse-AIDS. But does the funding of Hitler by the Rockefeller Foundation remains a mystery?
Hitler Financed By The Rockefeller Foundation with the shares of IG Farben
In 1915, focused oil billionaire Rockefeller, of "The Rockefeller Foundation," invested in the lee of the general interest of billions of dollars, targeting of building a new billion industry, the pharmaceutical investment industry. The US Rockefeller cartel and the German IG Farben cartel – consisting of Bayer, BASF and Hoechst collided with enormous greed.
On November 9, 1929, came a historic agreement: Two cartels divided the world among themselves. The Rockefeller group is assigned the global oil trade, IG Farben, the global trade in chemicals. But IG Farben did not comply with this agreement. Bayer, BASF, and Hoechst were the main sponsors of the election campaign for the seizure of power by Hitler.
The investments of IG Farben in this politician seemed to pay off initially. Every time the Wehrmacht invaded a European country, IG Farben was already on the foot board and annexed, especially on chemical industries and oil fields in Europe.
It is not surprising that the rulers want us still to delude that World War II had been a kind of accident of history, caused by a psychopath named Hitler. But the fact is that the wars, as today, initially had economic motives. "The Second World War was no exception. That would not have been possible without the economic interests of IG Farben and the seizure of power of Hitler." Said chief prosecutor, Telford Taylor, in the War Crimes Tribunal at Nuremberg against 24 managers of IG Farben.
Fritz Ter Meer and other IG Farben executives were sentenced to several years in prison, instead of a complete breakdown of the IG Farben cartel and the Allies, Moffelden the group under the influence of the Rockefeller group away.
The Rockefeller Group increased on the share package of IG Farben. The board of the resurrected pharmaceutical and chemical companies Bayer, BASF and Hoechst took the former war criminal Ter Meer aboard.
The leader of this audacious staffing, taking decisions was Ludwig Erhard, the future founder of the German economic miracle and Chancellor. He openly stated that the choice of the postwar leading figure in business is made after consideration of the special knowledge and experience of the manager of IG Farben, the commercial and chemical-technical field. The former husband of IG Farben, Ludwig Erhard, quickly became Minister of Economic Affairs, in post-war Germany, and in 1963 he even became Chancellor.
1963 was not only the year when Ludwig Erhard became Chancellor and war criminal Fritz Ter Meer again took the position of chairman of the supervisory board of Bayer Group. It was also the year in which the German Government the auspices of the so-called committee of experts for the protection of the global pharmaceutical interests within the framework of the United Nations took over. They were then known as "Codex Alimentarius".
Ir. Henri W.A. Deterding Challenges Standard Oil
Before WW I, the British government began to challenge Standard Oil for world oil supremacy. Rockefeller would not brook any competition to his monopoly so he sent vast sums of money to his homeland in Germany. The Kaiser used this money to arm Germany and keep the war going for 4 years. Here is a brief quote from a fascinating book entitled "We Fight for Oil."
"Lord Fisher not only discovered the method. He found the man. The man was a Holland clerk. He was rising as an official in the Royal Dutch Petroleum Company. The Admiral described this gentleman to the British Government as "Napoleonic in his audacity and Cromwellian in his thoroughness."
Henri W. A. Deterding was the name of this new Napoleon. He lived up to Lord Fisher's description. He extended the oil holdings of Royal Dutch into a dozen countries. He arranged for increased British capital control of this international trust. He merged the British Shell oil group with it, making of the two largest European organizations, a united Dutch-Shell combine, the strongest in the world. He became a British subject. The British Government made him Sir Henri and began to make British foreign policy.
By 1913, according to Lord Fisher's Memorandum at the time, Sir Henri "confessed" to the British Royal Commission on oil issue that: He had in Romania, Russia, California, Trinidad, Dutch Indies, and Mexico oil interests. The Anglo-Persian Company also said he was getting Mesopotamia and squeezing Persia, which was practically untouched areas of immense size reeking with oil.
Sir Thomas Browning said in his evidence that, the Royal Dutch-Shell combination is more powerful and aggressive than ever and was the great Standard Oil Trust of America. Let us, therefore, listen with deep attention to the words of a man [Deterding] who has power and sole control over the most powerful organization on earth, for the production of oil and source of everything. (Denny, We Fight for Oil, p. 25)."
Rockefeller financed "Cousin" Adolf Hitler
When WW I was over, the oil wars continued. The British had control of the Mosul and Iraqi fields and Standard Oil was willing to do ANYTHING to get them. Sir Henri Deterding was challenging Standard Oil even in the United States. Rockefeller resorted to his old trick of arming his homeland once again. Billions flowed to the Hitler regime from Rockefeller and Wall St.
Hitler was a Rothschild
Adolf Hitler was the grandson of Baron Rothschild.
Alois Hitler, the father of Adolf, was the illegitimate son of Maria Anna Schicklgruber and Baron Rothschild. There are some people who seriously doubt that Johann Georg Hiedler was the father of Alois. Thyssen and Koehler, for example, claim that Chancellor Dollfuss had ordered the Austrian police to conduct a thorough investigation into the Hitler's family. As a result of this investigation, a secret document was prepared that proved that Maria Anna Schicklgruber was living in Vienna at the time she conceived.
At that time Maria Anna was employed as a servant in the home of Baron Rothschild. As soon as the family discovered her pregnancy she was sent back to her home in Spital where Alois was born. If it is true that one of the Rothschilds is the real father of Alois Hitler, it would make Adolf a quarter Jew. According to these sources, Adolf Hitler knew of the existence of this document and the incriminating evidence it contained.
In order to obtain it he precipitated events in Austria and initiated the assassination of Dollfuss. According to this story, he failed to obtain the document at that time since Dollfuss kept it secret and had told Schuschnigg of its whereabouts so that in the event of his death the independence of Austria would remain assured. Several stories of this general character are in circulation. (Langer, The Mind of Adolf Hitler, p.107). This fact alone explains why the dunce Hitler should come from nowhere and become Chancellor of Germany.
David Rockefeller takes picture of "Cousin" Adolf Hitler
Picture legend:
"Hitler striding through the streets of Munich in Dec. 1937. I (David Rockefeller) pushed my way to the front of the crowd and snapped the photo just as the dictator passed." (Rockefeller, Memoirs). Rockefeller agrees to help Great Britain in WW II in exchange for Mideast oil fields.
When Hitler was about to invade Britain, the Rockefeller-controlled State Department agreed to help them with war material if Britain agreed to give them the Mideast oil fields after the war….What a dirty double-cross that would even make the Rockefeller founded Mafia ashamed.
Collier, Peter & Horowitz, David, The Rockefellers, An American Dynasty, Holt, Rinehart and Winston, New York, 1976.
Denny, Ludwell, We Fight of Oil, Alfred A. Knopf, New York, 1928.
Kemmer, Edwin Walter, The ABC of the Federal Reserve System, Greenwood Press Publishers, Westport, Conn., 1950.
Lundberg, Ferdinand, America's 60 Richest Families, The Vanguard Press, New York, 1937.
Langer, Walter, C., The Mind of Adolf Hitler, New American Library, New York, 1972.
Rockefeller, David, Memoirs, Random House, New York, 2002.
Springmeier, Fritz, Bloodlines of the Illuminati, Ambassador House, Austin, Texas, 2002.


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The Top Worst classic Film Accents: The British edition, Vol. 1
The British accent, in film at least, is one of the most distinctive speaking voices on Earth. I'm referring not just to the standard classic received pronunciation of say, a John Gielgud, or a Lawrence Olivier, but also to the strong Cockney of Michael Caine, or of the Scots of a chap like Sean Connery. Oh, how they get butchered! By Americans especially...wow.
Though the reverse can also be true, the British can generally pull of an American accent pretty well; I doubt that many average Americans know that Hugh Laurie from the telly programme HOUSE is English, but everyone knows that Renée Zellweger is very most certainly not, and if they don't, the firing squad is promptly at dawn.
Here is a list of five classic film bad accents. Dick Van Dyke is not included, because, well, his Cockney is legendary in badness.
"We are NOT amused"
In reverse order, from the somewhat tolerable, to the completely intolerable.
#5) Michael Caine: ZULU.
I love this bloke. Great personality. Charisma. Great movies. He's English, so what's the problem? Well his supposedly upper-crust accent in this legendary 1964 film Zulu is just plain bad. It's like some American trying to tell his best friend what Bertie Wooster sounds like. The film gets the big thumbs up, but Cockney Caine's fey r. p. gets both thumbs down!
#4) Burt Lancaster: ZULU DAWN.
Really? As Lt. colonel Anthony William Durnford? An Irishman? Really? Sadly, yes. The Irish, Aussie, and Sottish accents are much abused by Americans in general, and I apologise on behalf of my fellow Yanks for such abominations. Still a very good 'prequel' to Zulu, from 1979. It has a great to very good cast, and covers the topic effectively.
#3) Charlton Heston: KHARTOUM.
Ok...look at the cast of this film. Laurence Olivier. Ralph Richardson. Michael Hordern. Richard Johnson. Nigel Green. Douglas Wilmer. The best of the best in British acting. So who do they cast as Gen. Charles 'Chinese' Gordon, who was killed in Sudan in defense of Khartoum? Heston! He sounds like he's talking with a pencil betwixt his teeth!
#2) Katherine Hepburn: THE AFRICAN QUEEN.
She almost got the primo spot on the list, but missed it just barely. When I first saw this film, I was certain that they had overdubbed two chickens having a nice barnyard chat. I suppose the director had her do this to cover up the worst performance in Humphrey Bogart's career, but I have no proof of this. As it stands, it's faux-English gobble-squawk at its worst.
And finally, the absolute worst, and will be considered the worst on all subsequent editions of the list:
#1) Ingrid Berman: DR. JECKYL & MR. HYDE.
I'm no fan of Spencer Tracy movies in general, but this 1941 (so-called) adaptation of one of my favourite novels, is just plain tepid. It's also quite preachy, as well. The main problem, beside all others, is that we have the Swedish actress Ingrid Bergman b-a-d-l-y playing a Cockney woman! Ol' Dick Van Dyke, in comparison, sounds like Michael Caine on holiday in the old neighborhood!
...and I don't mean the House of Lords, mate, innit?

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text:
\section{Introduction}
``Pathogenic Social Media'' (PSM) accounts have the capability of spreading disinformation to viral proportions. These accounts including terrorist supporter accounts, water armies, and fake news writers seek to promote or degrade certain ideas through social media in order to reach their goals. In this regard,  identifying PSM accounts has found  growing important applications to countering extremism~\cite{khader2016combating,allcott2017social}, detection of water armies~\cite{chen2013battling,chen2013best}  and fake news campaigns~\cite{kang2016fake,allcott2017social}. In Twitter, many of these accounts are social bots.

\begin{table}[ht]
	\centering
	\caption{\textmd{Label propagation selection approach - number of selected users}}
	{
		\begin{tabular}{|l| c c c |}
			\hline
			\textbf{Method} & \textbf{False Pos} & \textbf{True Pos} & \textbf{Precision}
			\\ \hline \hline
			$All\ features$ &164,012&31,131&\textbf{0.16}\\ \hline
			$No\ content$ &357,027&63,025&0.15\\ \hline	\hline
			$\epsilon_{K\&M}$ & 9,305 & 14,176 & 0.60
			\\ \hline
			$\epsilon_{rel}$ & 561 &  498 & 0.47
			\\ \hline
			$\epsilon_{nb}$ & 1,101 & 1,768 & 0.62
			\\ \hline
			$\epsilon_{wnb}$ & 1,318  & 4,000 & \textbf{0.75
			\\ \hline  \hline
	\end{tabular}}
	\label{tab:pro}
\end{table}

Early detection of PSM accounts are crucial as they are likely to be the key users to malicious information campaign and detecting them is thus critical to understanding and stopping such campaigns. However, this task is difficult in practice. Existing methods rely on message content~\cite{morstatter2016new}, network structure~\cite{cao2012aiding} or a combination of both~\cite{subrahmanian2016darpa,davis2016botornot,dickerson2014using}. However, the network structure is not always available. For example, the Facebook API does not make this information available without the permission of the users.
Moreover, the use of content often necessitates the training of a new model for the previously unobserved topics. For example, PSM accounts taking part in elections in the U.S. and Europe will likely leverage different types of content. To deal with these issues, causal inference is tailored to detect PSM accounts  in~\cite{shaabani2018detecting} based on an unsupervised learning method. The authors identified PSM users in the \emph{viral cascades}, where ``viral" is defined as an order-of-magnitude increase. As viral cascades are so rare, users that cause them are suspicious accounts. This work is continued by considering a time-decay causal and proposing a causal community detection-based classification method~\cite{alvari2018early}.

In this paper, we expand on the previous work in \cite{shaabani2018detecting} and propose graph-based metrics to distinguish PSMs from normal users within a short time around their activities. Our new metrics combined with our causal ones can achieve high precision 0.90, while increasing the recall from 0.22 to 0.49. We propose supervised and semi-supervised approaches and then show our proposed methods outperform the ones in the literature. In summary, the major contributions of this paper are itemized as follows:

\begin{itemize}
	\item  We propose supervised and semi-supervised PSM detection frameworks that do not leverage network structure, cascade path information, content and user's information.

	\medspace
	\item We introduce graph-based framework using the cascades and propose a series of scalable metrics to identify PSM users. We apply this framework to more than 722K users and 35K cascades.

	\medspace
	\item We propose a deep neural network framework which  achieves AUC of 0.82. We show that our framework significantly outperforms Sentimetrix~\cite{subrahmanian2016darpa} (0.74), causality~\cite{shaabani2018detecting} (0.73), time-decay causality~\cite{alvari2018early} (0.66), and causal community detection-based classification~\cite{alvari2018early}~(0.6).

	\medspace
	\item  We introduce a self-training semi-supervised framework that can capture more than 29K PSM users with the precision of 0.81. We  only used 600 labeled data for training and development sets. Moreover, if a supervisor is involved in the training loop, the proposed algorithm is able to capture more than 80K PSM users.

\end{itemize}

The rest of the paper is organized as follows. In Section~\ref{sec:techapp}, we describe our framework that leverages causal metrics, graph-based metrics. We present the algorithms in Section~\ref{sec:algo}. This is followed by a description of our dataset in Section~\ref{sec:isisdata}. Then we describe our implementation and discuss our results in Section~\ref{sec:res}. Finally, related work is reviewed in Section~\ref{sec:rw}.

\section{Technical Approach} \label{sec:techapp}

\subsection{Technical Preliminaries}
Throughout this paper we shall represent cascades as an ``action log" ($Actions$) of tuples where each tuple $(u,m,t) \in Actions$ corresponds with a user $u \in U$ posting message $m \in M$ at time $t \in T$, following the convention of~\cite{goyal2010learning, shaabani2018detecting}. We assume that set $M$ includes posts/repost of a certain original tweet or message. For a given message, we  only consider the first occurrence of each user. We define $Actions_m$ as a subset of $Actions$ for a specific message $m$. Formally, we define it as $Actions_m = \{(u', m', t') \in Actions \ s.t.\ m'=m\}$.

\begin{defn} \emph{\textbf{($m$-participant).}}\label{def:cascade}
	For a given $m\in M$, user~$u$ is an \textbf{$m$-participant} if there exists $t$ such that $(u,m,t) \in Actions$.
\end{defn}

Note that the users posting tweet/retweet in the early stage of cascades are the most important ones since they play a significant role in advertising the message and making it viral. For a given $m\in M$, we say $m$-participant $i$ ``precedes" $m$-participant $j$ if there exists $t<t'$ where $(i,m,t), (j,m,t') \in Actions$. Thus, we define \textit{key users} as a set of users adopting a message in the early stage of its life span. We formally define \textit{key user} as follows:
\begin{defn}\emph{\textbf{(Key User).}}\label{def:kusers}
	For a given message $m$, $m$-participant $i$, and $Actions_m$, we say user $i$ is a \textbf{key user} iff user $i$ precedes at least $\phi$ fraction of $m$-participants (formally: $ |Actions_m| \times \phi \le |\{j |  \exists t': (j, m, t') \in \ Actions_m\, \wedge\, t' > t \}|$, $(i, m, t) \in Actions_m$), where $\phi \in (0,1)$.
\end{defn}
The notation  $|\cdot|$ denotes the cardinality of a set. All messages are not equally important. That is, only a small portion of them gets popular. We define \textit{viral messages} as:

\begin{defn} \emph{\textbf{(Viral Messages).}}\label{def:cascade}
	For a given threshold $\theta$, we say that a message $m \in M$ is \textbf{viral} iff $|Actions_m| \ge \theta$. We use $M_{vir}$ to denote the set of viral messages.
\end{defn}

The Definition~\ref{def:cascade} allows us to compute the prior probability of a message (cascade) going viral as follows:
\begin{equation}
	\rho = \frac{|M_{vir}|}{|M|}
\end{equation}

We also define the probability of a cascade $m$ going viral given some user $i$ was involved as:

\begin{equation}
	p_{m | i} = \frac{|\{m \in M_{vir}\, s.t.\ i\ is\  a \  key \ user\}|}{|\{m \in M\, s.t.\ i\ is\  a \  key \ user\}|}
\end{equation}

We are also concerned with two other measures. First, the probability that two users $i$ and $j$ tweet or retweet viral post $m$ chronologically, and both are key users. In other words, these two users are making post $m$ viral.
\begin{equation}\label{eq:rule1}
	p_{i,j} =
	\frac{
		\splitfrac{
			|\{m \in M_{vir} | \exists t,t'\, where\, t<t'\, and
		}
		{
			\, (i,m,t),(j,m,t') \in Actions\}|
		}
	}
	{
		\splitfrac{
			|\{m \in M | \exists t,t'\, where\, t<t'\ and
		}
		{
			\ (i,m,t),(j,m,t') \in Actions\}|
		}
	}
\end{equation}

Second, the probability that key user $j$ tweets/retweets viral post $m$ and user $i$ does not tweet/retweet earlier than $j$. In other words, only user $j$ is making post $m$ viral.
\begin{equation}\label{eq:rule2}
	p_{\neg{i},j} = \frac{
		\splitfrac{
			|\{m \in M_{vir} | \exists t'\, s.t.\, (j,m,t') \in Actions\, and
		}
		{
			\, \not\exists t\  where\ t <t',\ (i,m,t) \in Actions\}|
		}
	}
	{
		\splitfrac{
			|\{m \in M | \exists t'\, s.t.\, (j,m,t') \in Actions\, and
		}
		{
			\, \not\exists t\  where\ t<t', \ (i,m,t) \in Actions\}|
		}
	}
\end{equation}

Knowing the action log, we aim to find a set of pathogenic social media (PSM) accounts. These users are associated with the early stages of large information cascades and, once detected, are often deactivated by a social media firm. In the causal framework, a series of causality-based metrics for identifying PSM users is introduced.

\subsection{Causal Framework}
We adopt the causal inference framework previously introduced in~\cite{suppes1970probabilistic,kleinberg2009temporal}. We expand upon that work in two ways~\cite{shaabani2018detecting}: (1.) we adopt it to the problem of identifying PSM accounts and (2.) we extend their single causal metric to a set of metrics. Multiple causality measurements provide a stronger determination of significant causality relationships.

For a given viral cascade, we seek to identify potential users who likely \textit{cause} the cascade viral. We first require an initial set of criteria for such a causal user.  We do this by instantiating the notion of Prima Facie causes to our particular use case below:

\begin{defn} \emph{\textbf{(Prima Facie Causal User).}}\label{def:pf}
	A user $u$ is a prima facie causal user of cascade $m$ iff: User $u$ is a key user of $m$,  $m \in M_{vir}$, and $p_{m|u} > \rho$.
\end{defn}

For a given cascade $m$, we will often use the language \textit{prima facie causal user} to describe user $i$ is a prima facie cause for $m$ to be viral. In determining if a given prima facie causal user is causal, we must consider other ``related" users. We say $i$ and $j$ are $m$-related if (1.) $i$ and $j$ are both prima facie causal users for $m$, (2.) $i$ and $j$ are both key users for $m$, and (3.) $i$ precedes $j$. Hence, we will define the set of ``related users" for user $i$ (denoted $R(i)$) as follows:
\begin{equation}\label{eq:R}
	R(i) = \{j \ s.t. \ j \not= i\ , \exists m \in M \ s.t. \ i,j \ are \ m-related\}
\end{equation}

Therefore, $p_{i,j}$  in (\ref{eq:rule1}) is the probability that cascade $m$ goes viral given both users $i$ and $j$, and $p_{\neg i,j}$ in (\ref{eq:rule2}) is the probability that cascade $m$ goes viral given key user $j$ tweets/retweets it while key user $i$ does not tweet/retweet $m$ or precedes $j$. The idea is that if $p_{i,j} - p_{\neg i,j} > 0$, then user $i$ is more likely a cause than $j$ for $m$ to become viral. We measure \textit{Kleinberg-Mishra causality} ($\epsilon_{K\&M}$) as the average of this quantity to determine how causal a given user $i$ is as follows:
\begin{equation}
	\epsilon_{K\&M}(i) = \frac{\sum_{j\in R(i)} (p_{i,j} - p_{\neg i, j})}{|R(i)|}
\end{equation}
Intuitively, $\epsilon_{K\&M} $ measures the degree of causality exhibited by user $i$.  Additionally, we find it useful to include a few other measures. We introduce \textit{relative likelihood causality} ($\epsilon_{rel}$) as follows:
\begin{equation}
	\epsilon_{rel}(i) = \frac{\sum_{j\in R(i)} S(i,j)}{|R(i)|}
\end{equation}
\begin{equation}
	S(i, j)=
	\begin{cases}
		(\frac{p_{i,j}}{p_{\neg i, j} + \omega})-1 , & p_{i,j}> p_{\neg i, j}\\
		0, & p_{i,j} = p_{\neg i,j'}\\
		1- (\frac{p_{\neg i, j}}{p_{i,j}}), &\text{otherwise}
	\end{cases}
\end{equation}

where $\omega$ is infinitesimal. Relative likelihood causality metric assesses the relative difference between $p_{i,j}$ and $p_{\neg{i},j}$. This helps us to find new users that may not be prioritized by $\epsilon_{K\&M}$.
We also find that if a user is mostly appearing after those with the high value of $\epsilon_{K\&M}$, then it is likely to be a PSM account. One can consider all possible combinations of events to capture this situation. However, this approach is computationally expensive. Therefore, we define $\mathcal{Q}(j) $ as:
\begin{equation}\label{eq:Q}
	\mathcal{Q}(j) = \{i \ s.t. \ \ j \in R(i)\}
\end{equation}

Accordingly, we define  \textit{neighborhood-based causality}~($\epsilon_{nb}$) as the average $\epsilon_{K\&M}(i)$ for all $i \in Q(j)$ as follows:
\begin{equation}
	\epsilon_{nb}(j) = \frac{\sum_{i \in \mathcal{Q}(j)} \epsilon_{K\&M} (i)}{|\mathcal{Q}(j)|}
\end{equation}

The intuition behind this metric is that accounts who are retweeting a message that was tweeted/retweeted by several causal users are potential for PSM accounts. We also define the \textit{weighted neighborhood-based causality}~($\epsilon_{wnb}$) as follows:
\begin{equation} \label{eq:weighted}
	\epsilon_{wnb} (j) = \frac{\sum_{i \in \mathcal{Q}(j)} w_i \times \epsilon_{K\&M} (i) }{\sum_{i \in \mathcal{Q}(j)} w_i}
\end{equation}

The intuition behind the metric $\epsilon_{wnb}$ is that the users in $\mathcal{Q}$ may not have the same impact on user $j$ and thus different weights $w_i$ are assigned to each user $i$ with~$\epsilon_{K\&M}(i)$.
In our previous work \cite{shaabani2018detecting}, we provided more details of these metrics.

\subsection{Graph-based Framework}
\noindent\textbf{User-Message Bipartite Graph.} Here, we denote $Actions$ as a bipartite graph $G_{u-m}(U, M, E)$, where  users $U$ and messages $M$ are disjoint sets of vertices. There is an annotated link from user $u$ to message $m$ if $u$ has tweeted/retweeted $m$ and is annotated by occurrence time $t$ (see Fig. \ref{fig:graph}). In other words, every edge in graph $G_{u-m}$ is associated with one tuple $(u,m,t) \in Actions$. For a given node $u \in U (m \in M)$, the set $\mathcal{N}_u= \{m' \in M\ s.t.\ (u,m') \in E\} (\mathcal{N}_m= \{u' \in U\ s.t.\ (u',m) \in E\} )$ is the set of immediate neighbors of $u$ ($m$). We also define  $U^v \subset U$ which is the set of verified users (often celebrities). We indicate $U^v_m = \{u|(u,m,t)\in Actions, u \in U^v\}$ as a set of verified users that have re/tweeted message $m$.

\begin{figure}
	\centering
	\includegraphics[width=0.7\columnwidth]{bare_adv.png}
	\caption{User-message bipartite graph and user graph. Red and purple nodes represent users and messages respectively. Messages are labeled with the length of the cascade (degree). Blue and Green edges represent user-message and user-user relationships.}
	\label{fig:graph}
\end{figure}

As for the edges, we examine different metrics such as Jaccard similarity between users, and rank of a user in a message which is defined as $Rank_{(u,m)} = |\{(u',m,t')\in Actions| (u,m,t) \in Actions, t'<t \}|$.  We also define normalized rank as:
\begin{equation}
NR(u,m) = 1- \frac{Rank_{(u,m)}}{\mathcal{N}_m}
\end{equation}

Our intuition behind rank metric $Rank_{(u,m)}$ is that the earlier a user has participated in spreading a message, the more important the user is. In this regard, we can also define the exponential decay of the time as:

\begin{equation}
\mathcal{T}_u^m = \exp (-\gamma \Delta t_u^m)
\end{equation}

where $\Delta t_u^m = \{t|(u,m,t)\in Actions\} - \min(\{t'|(u',m,t')\in Actions\})$, and $\gamma$ is a constant. This metric prioritizes based on the retweeting time of the message. In other words, this metric assigns different weights to different time points of a given time interval, inversely proportional to their duration from start of the cascade, i.e., smaller duration is associated with higher weight.

Using all these information, we then annotated users $U$ based on their local and network characteristics such as degree, and PageRank. We also consider function $ \mathcal{F} \in \{sum,max, min, avg, med, std\}$ to calculate statistics such as minimum, mean, median, maximum, and standard deviation based on their one-hop or two-hops neighbors. For example, for a given user $u$, mean of re/tweeted message's PageRank of user $u$ proved to be among top predictive metrics according to our experiments. Using these intuitions, we explored the space of variants features and list those we found to be best-performing in Table~\ref{tab:um_metrics}.

\begin{table}[ht]
	\centering
	\caption{\textmd{User-Message Bipartite Graph-Based Metrics}}
	\begin{tabular}{| p{3.5cm}| p {4cm}|}
		\hline
		\textbf{Name} & \textbf{Definition}   \\\hline \hline
		\vspace{0cm}	 	Degree &  \vspace*{-3mm}	\begin{equation}\nonumber
		\splitfrac{D_v = }{ |\{v'|(v,v') \in E \vee (v',v) \in E \}|}
		\end{equation}
		 \vspace*{-3mm}
		\\ \hline
		\vspace{0cm} Cascade size statistics &  \vspace*{-1mm}\begin{equation}\nonumber
		CS_{u,\mathcal{F}} = \mathcal{F}_{m \in \mathcal{N}_u} D_m
		\end{equation} \vspace*{-3mm}
		\\ \hline
		\vspace{2mm}	PageRank&  \vspace*{-2mm}	\begin{equation}\nonumber
		\splitfrac{PR(v) = }{\frac{1-d}{N}+d \sum_{v' \in \mathcal{N}_v} \frac{PR(v')}{L(v')} }
		\end{equation}\vspace*{-2mm}\\\hline
		\vspace{0cm}	Message's PageRank statistics &\vspace*{-3mm}\begin{equation}\nonumber
		PS_{u,\mathcal{F}} = \mathcal{F}_{m \in \mathcal{N}_u} PR(m)
		\end{equation}\vspace*{-3mm}\\ \hline
		\vspace{0cm}	Number of verified users & \vspace*{-3mm} \begin{equation}\nonumber
		\splitfrac{Vr_m =}
		{|\{u|(u,m) \in E, u\in U^v\}| }
		\end{equation}\vspace*{-3mm}\\ \hline
		\vspace{0cm}	Jaccard similarity statistics & \vspace*{-3mm}\begin{equation}\nonumber
		JS_{u,\mathcal{F}} = \mathcal{F}_{u' \in U } \frac{|\mathcal{N}_u \cap \mathcal{N}_{u'}|}{|\mathcal{N}_u \cup \mathcal{N}_{u'}|}
		\end{equation}\vspace*{-3mm}
		\\ \hline
		\vspace{0cm}	Intersection statistics & \vspace*{-3mm}\begin{equation}\nonumber
		IS_{u,\mathcal{F}} = \mathcal{F}_{u' \in U } |\mathcal{N}_u \cap \mathcal{N}_{u'}|
		\end{equation}\vspace*{-3mm}
		\\ \hline
		\vspace{0cm}	Normalized rank statistics & \vspace*{-3mm}\begin{equation}\nonumber
		NRS_{u,\mathcal{F}} = \mathcal{F}_{m \in \mathcal{N}_u} NR{(u,m)}
		\end{equation}\vspace*{-3mm}
		\\ \hline
		\vspace{0cm}	$\mathcal{T}$ statistics & \begin{equation}\nonumber
		\mathcal{T} S_{u,\mathcal{F}} = \mathcal{F}_{m \in \mathcal{N}_u} \mathcal{T}_u^m
		\end{equation}\vspace*{-3mm}\\ \hline
		\vspace{0cm}	Verified users in the
		cascades statistics &\vspace*{-2mm}  \begin{equation}\nonumber
		U^v S_{u,\mathcal{F}} = \mathcal{F}_{m \in \mathcal{N}_u} |U^v_m|
		\end{equation}\vspace*{-3mm}\\ \hline
		\hline
	\end{tabular}
	\label{tab:um_metrics}
\end{table}

\noindent\textbf{User Graph.}
We represent a directed weighted user graph $G(V', E')$ where the set of nodes $V'$ corresponds with key users. There is a link between two users if they are both key users of at least a message. There is a link from $i$ ($j$) to $j$ ($i$) if the number of times that ``$i$ appears before $j$ and both are key users'' is equal to or larger (smaller) than the case when ``$j$ appears before $i$'', see Fig. \ref{fig:graph}. For a given node $i$, the set $N_i^{out}= \{i' \in V'\ s.t.\ (i,i') \in E'\}$ ($N_i^{in}= \{i' \in V'\ s.t.\ (i',i) \in E'\}$)- the set of outgoing (incoming) immediate neighbors of $i$. The weight of edges is determined as a variant of co-occurences of the key user pairs:
\begin{equation}
\mathcal{CO}_{i,j} = \frac{
	\splitdfrac{|\{m|i,j\ are\ key\ users,\ \exists t,t'\ where\ t<t',}
	{ (i,m,t), (j,m,t') \in Actions\}|}}
{\min(|\{m|i\ is\ a\ key\ user\}|, |\{m|j\ is\ a\ key\ user\}|)}
\end{equation}

Using $\mathcal{CO}_{i,j}$, we then propose a weighted co-occurrence score for user $i$ as:
\begin{equation}
\mathcal{CO}^w_{i, N_i} = \frac{\sum_{j \in N_i} (abs(\delta_{i,j}) +1) \times \mathcal{CO}_{i,j} }
{\sum_{j \in N_i} (abs(\delta_{i,j}) +1)}
\end{equation}
where $abs(\cdot)$ denotes the absolute value of the input. The differences between ordered joint occurrences $\delta_{i,j}$ is also defined as:
\begin{equation}
\splitdfrac{\delta_{i,j} =}{
	\splitdfrac{ |\{m| \exists t,t'\ s.t.\ t<t', (i,m,t), (j,m,t') \in Actions\}|}
	{-|\{m| \exists t,t'\ s.t.\ t>t', (i,m,t), (j,m,t') \in Actions\}|}}
\end{equation}
The list of user graph-based metrics extracted from graph $G$ is shown in Table~\ref{tab:uu_metrics}.
\begin{table}[ht]
	\centering
	\caption{\textmd{User Graph-Based Metrics}}
	\begin{tabular}{|p{3.5cm}| p {4cm}|}
		\hline
		\textbf{Description} & \textbf{Definition}   \\\hline \hline
		\vspace{0cm} Degree & \vspace*{-1mm} \begin{equation}\nonumber
		|N_i^{out}|
		\end{equation}\vspace*{-3mm}\\ \hline
		\vspace{0cm}	Outgoing co-occurrence score statistics & \vspace*{-3mm} \begin{equation}\nonumber
		\mathcal{CO} S_{i,\mathcal{F}}^{out} = \mathcal{F}_{j \in \mathcal{N}_i^{out}} \mathcal{CO}_{i,j}
		\end{equation}\vspace*{-3mm}\\ \hline
		\vspace{0cm}	Incoming co-occurrence score statistics&\vspace*{-3mm} \begin{equation}\nonumber
		\mathcal{CO} S_{i,\mathcal{F}}^{in} = \mathcal{F}_{j \in \mathcal{N}_i^{in}} \mathcal{CO}_{i,j}
		\end{equation}\vspace*{-3mm}\\ \hline
		\vspace{0cm}	Weighted co-occurrence score & \vspace*{-1mm}\begin{equation}\nonumber
		\mathcal{CO}^w_{i, N_i^{out}}
		\end{equation}\vspace*{-3mm}\\ \hline
		\vspace{0cm}	Number of outgoing verified users &\vspace*{-2mm} \begin{equation}\nonumber
		|\{j|j\in N_i^{out}, j \in U^v\}|
		\end{equation}\vspace*{-3mm}\\ \hline
		\vspace{0cm}	Number of incoming verified & \vspace*{-1mm}\begin{equation}\nonumber
		|\{j|j\in N_i^{in}, j \in U^v\}|
		\end{equation}\vspace*{-3mm}\\ \hline
		Triangles & Number of triangles\\ \hline
		\vspace{0cm}	Clustering coefficient & \vspace*{-3mm}\begin{equation}\nonumber
		\splitdfrac{CC_i = }
		{\frac{|\{(j,k) | j,k \in N_i, (j,k) \in E'  \}|}{|N_i| \times (|N_i|-1)}}
		\end{equation}\vspace*{-1mm}\\ \hline

		\hline
	\end{tabular}
	\label{tab:uu_metrics}
\end{table}
We further calculate the probability of ``user $j$ appears after user $i$'' as:
\begin{equation}
P_{(j,i)} = \frac{\splitfrac{
		|\{m \in M_{vir} | \exists t,t'\, where\, t<t'\, and
	}
	{
		\, (i,m,t),(j,m,t') \in Actions\}|
} }{|\{m|(j,m,t) \in Actions\}|}
\end{equation}
The average probability that user $i$ appears before its related users $R(i)$ is also a good indicator for identifying PSM accounts:
\begin{equation}
CM_i = \frac{\sum_{R(i)} P_{(j,i)}}{|R(i)|}
\end{equation}
We aim to evaluate users from different perspectives and these metrics have shown to be helpful for evaluating users and detecting PSM accounts.

\subsection{Problem Statement}
Our goal is to find the potential PSM accounts from the cascades. In the previous section, we discussed causality metrics, and defined diverse set of features using both user-message bipartite and user graphs where these metrics can discriminate the users of interest.

\noindent{\textbf{Problem. (Early PSM Account Detection).}} \label{prob:1}
\emph{	Given Action log Actions, causality and structural metrics, we wish to identify set of key users that are PSM accounts.}

\section{PSM Account Detection Algorithm}\label{sec:algo}
We employ supervised, and semi-supervised approaches for detecting PSM accounts. Proposed metrics are scalable and can be calculated efficiently using map-reduce programming model and storing data in a graph-based database. To such aim, we used Neo4j to store data and calculated most of the structural metrics using Cypher query language~\cite{webber2018programmatic}.

\subsection{Supervised Learning Approach}
We evaluate several supervised learning approaches including logistic regression (LR), naive bayes (NB), k-nearest neighbors (KNN) and random forest (RF) on the same set of features. We also develop a dense deep neural network structure using Keras. As for the deep neural network and in order to find the best architecture and hyperparamters, we utilize the random search method. Many model structures were tested and Fig.~\ref{fig:Dnn-Structure} illustrates the best architecture.
\begin{figure}[ht]
	\centering
	\includegraphics[width=.7\columnwidth]{dnn_structure.png}
	\caption{The proposed deep neural net structure}
	\label{fig:Dnn-Structure}
\end{figure}

As we can see from Fig. \ref{fig:Dnn-Structure}, the proposed deep neural net, in fact, consists of three dense deep neural net structures. The first two structures are of the same, but the activation functions of their layers are different. The intuition is that we aimed to capture the most useful information from the input data and our experiments show the ReLU and Sigmoid activation functions can contribute to this.  Specifically, these two structures are aimed to filter the noises in the input data and prepare clean inputs to feed into the third  structure. In this regard, the outputs of these two structures along with the input data are   concatenated into one vector and this vector is fed into another dense deep neural net.  Finally, the output of this structure  is fed into a regular output layer.  To avoid overfitting, we used dropout method.
In the proposed framework, the binary cross entropy loss function is minimized and the best optimizers are reported as Adam and Adagrad.

\subsection{Self-Training Semi-Supervised Learning Approach}

Semi-supervised algorithms~\cite{subrahmanian2016darpa,zhu2006semi} use unlabeled data along with the labeled data to better capture the shape of the underlying data distribution and generalize better to new samples. Here, we propose a \emph{Weighted Self-Training Algorithm} (\textsc{WSeT}) shown in Algorithm~\ref{alg:WSeT} to address such problem. We start with small amount of labeled training data and iteratively add users  with high confidence scores from unlabeled data to the training set. Lets denote labeled data $L=\{\textbf{u}_i, l_i\}$ and unlabeled data $U=\{\textbf{u}_j\}$. Labeled data is split to training set $L_t$ and development set $L_d$. We then iteratively train a classifier using training set and predict the confidence scores for development set and unlabeled data.  Based on the \emph{confidence score} obtained from  \emph{development set},  a \emph{threshold} is determined. We then select \emph{all samples} from \emph{unlabaled data} that \emph{satisfy the threshold}. Next, those samples are \emph{removed}  from \emph{unlabeled set} and are \emph{added}  to the \emph{training set}. The termination condition is determined based on  at most  $\theta_{tr}$ drop in accuracy on the development set or minimum number of selected users by algorithm.

\begin{algorithm}
	\caption{\small \textbf{Weighted Self-Training Algorithm (\textsc{WSeT})}}\label{alg:WSeT}
	{
		\begin{algorithmic}[1]
			\Procedure{WSeT}{$L=\{\textbf{u}_i, l_i\}, U=\{\textbf{u}_j\}, \alpha, \beta, \theta_{pr}, \theta_{tr}$}
			\State {Split $L$ to training set $L_t$ and development set $L_d$}
			\State {$L_t.w_c = 1$}
			\State {$it = 1$}
			\State {$m = $ Train a classification model using $L_t$}
			\State {$L_d.p=$ confidence score $p$ using $m$ of $L_d$}
			\State {$c =$ accuracy of model $m$ on $L_d$}
			\State{$c'=c$}
			\While {$c'>= c-\theta_{tr}$}
			\State {$U.p=$ confidence score $p$ using $m$ of $U$}
			\State {Update $L_t$ and $U$ by Algorithm~\ref{alg:UpDWSeT} ($L_t, L_d, U, \alpha,\beta, \theta_{pr}, it$) }
			\State {$m= $ Train a classification model using $L_t$}
			\State {$L_d.p=$ confidence score $p$ using $m$ of $L_d$}
			\State {$c' =$ accuracy of model $m$ on $L_d$}
			\State {$it = it + 1$}
			\EndWhile
			\State \textbf{return} $L_t$
			\EndProcedure
	\end{algorithmic}}
\end{algorithm}

There are still two main questions that need to be answered:
\begin{description}
\item[Q1.]   Should all training samples be weighted equally?
\item[Q2.] How should a threshold be determined for adding unlabeled data to the labeled set?
\end{description}

Since the prediction mistake reinforces itself, and the prediction error increases by number of iterations, the way we choose samples is of importance. According to our experiments, all training samples should not be weighted equally. We found the \emph{exponential decay weighting} approach as the most efficient one (see Q1). Considering a sample  with confidence $p_l$ associated to a specific label $l$ in iteration $it$, the exponential decay weighting approach  is  defined as:

\begin{equation}
\exp(-\beta \times it \times (\frac{1}{1-p_l}))
\end{equation}
where $\beta$ is a parameter. To answer the second question, we pick the threshold to have the minimum precision of $\theta_{pr}$ on development set in each iteration. Since the precision decreases as the algorithm iterates, the threshold is required to be adjusted in order to make sure  the top ranked and qualified samples are picked up. Mathematically, the updated threshold  in each iteration is defined as follows:

\begin{equation}
\theta_{pr}-\alpha \times (it-1)
\end{equation}
where $\alpha$ is a parameter, $\alpha \in [0, \frac{1}{it-1}], it>0$. We pick $0.005$ for the experiments. If $it=1$, the threshold  is equal to $\theta_{pr}$. As the number of iteration increases the  threshold is updated according to the product of $\alpha$ and iteration number $it$. This approach can make sure that we are picking samples with acceptable confidence. Algorithm~\ref{alg:UpDWSeT} presents our approach for updating labeled and unlabaled datasets.

\begin{algorithm}
	\caption{\small \textbf{Update Weighted Self-Training Datasets Algorithm (\textsc{UpDWSeT})}}\label{alg:UpDWSeT}
	{
		\begin{algorithmic}[1]
			\Procedure{UpDWSet}{$L_t, L_d, U, \alpha, \beta, \theta_{pr}, it$}
			\State $S=\emptyset$
			\For {$ l \in [True, False]$}
			\State {$thr = FindPrecisionThreshold (L_d, \theta_{pr}-\alpha \times (it-1) , label=l )$}
			\State {$S = S \cup \{\textbf{u} \in U|\textbf{u}.p\ge thr\}$}
			\EndFor
			\State {$U= U - S$}
			\State {$S.w_c = \exp(-\beta \times it \times (\frac{1}{1-p}))$}
			\State{$L_t = L_t \cup S$}
			\State \textbf{return} $L_t, U$
			\EndProcedure
	\end{algorithmic}}
\end{algorithm}

\section{ISIS  Dataset}\label{sec:isisdata}
Our dataset consists of ISIS related tweets/retweets in Arabic gathered from Feb. 2016 to May 2016. This dataset is discussed in details in~\cite{shaabani2018detecting}. The dataset includes tweets and the associated information such as user ID, re-tweet ID, hashtags, number of followers, number of followee, content, date and time. About 53M tweets are collected based on the 290 hashtags such as State of the Islamic-Caliphate, and Islamic State. In this paper, we only use tweets (more than 9M) associated with viral cascades.

\begin{figure}
	\centering
		\includegraphics[width=.4\columnwidth]{numCas_NumUsr.pdf}
	\caption{Distribution of cascades vs cascade size}
	\label{fig:dist_casc_all}
\end{figure}

\noindent{\textbf{Cascades.}} We aim to identify PSM accounts.  For this dataset, they are mainly social bots or terrorism-supporting accounts that participate in viral cascades. The tweets  have been retweeted from 102 to 18,892 times. This leads to more than 35K cascades which are tweeted or retweeted by more than 1M users. The distribution of the number of cascades vs cascade size is illustrated in Fig.~\ref{fig:dist_casc_all}.

\noindent{\textbf{User's Current Status.}} We select \textit{key users} that have tweeted or retweeted a post in its early life span - among first half of the users (according to Definition~\ref{def:kusers}, $\phi = 0.5$), and check whether they are active or not. Accounts are not active if they are suspended or deleted. Less than 24\% of the users are inactive.
 Inactive users are representative of automatic and terrorism accounts aiming to disseminate their propaganda and manipulate the statistics of the hashtags of their interest.

\section{Results and Discussion}\label{sec:res}
We implement part of our code in Scala Spark and Python 2.7x and run it on a machine equipped with an Intel Xeon CPU (2 processors of 2.4 GHz) with 256 GB of RAM running Windows 7. We also implement most of structural metrics in Cypher query language. We create the graphs using Neo4j~\cite{webber2018programmatic} on a machine equipped with an Intel Xeon CPU (2 processors of 2.4 GHz) with 520 GB of RAM. We set the parameter $\phi$ as 0.5 to label key users  (Definition~\ref{def:kusers}). That is, we are looking for the users that participate in the \emph{action} before the number of participants gets twice.

In the following sections, first we look at the baseline methods. Then we address  the performance of two proposed approaches (see Section~\ref{sec:algo}): (1) \textit{Supervised Learning Approach}: applying different supervised learning methods on proposed metrics, (2) \textit{Self-Training Semi-Supervised Learning Approach}: selecting users by applying Algorithm~\ref{alg:WSeT}. The intuition behind this approach is to select users with the high probability of being either PSM or non-PSM (normal user) from unlabeled data and then adding them to the training set in order to improve the performance. We evaluate methods based on both Precision-Recall and Receiver Operating Characteristics (ROC) curves. Note that in all experiments, the training, development, and test sets are imbalanced with more  normal users than PSM users. The statistics of the datasets are presented in Table~\ref{tab:dataset_exp}. Dataset $\mathcal{A}$ is randomly selected from dataset $\mathcal{B}$ using sklearn library~\cite{scikit-learn}. Note that, all random selections of data in the experiments have been done using sklearn library. We repeated the experiments 3 times and picked the median output. It is worth to mention that the variance among the results was negligible. In this problem, our goal is to achieve high precision while maximizing the recall. The main reason is labeling an account as PSM means it should be deleted. However, removing a  normal user is costly. Therefore, it is important to have a high precision to prevent removing the normal users.

\begin{table}[ht]
	\centering
	\caption{\small \textmd{Statistics of the datasets used in experiments.}}
	{
		\begin{tabular}{|c | c|c|c|}
			\hline
			\textbf{Name} & \textbf{ PSM accounts} & \textbf{Normal accounts} & \textbf{Total}\\ \hline \hline
			 $\mathcal{A}$ & 19,859  & 65,417 & 85,276\\ \hline
			$\mathcal{B}$ & 137,248 & 585,396 & 722,644\\ \hline
	\end{tabular}}
	\label{tab:dataset_exp}
\end{table}

\subsection{Baseline Methods}
We have compared our results with existing work for detecting PSM accounts~\cite{shaabani2018detecting,alvari2018early} or bots~\cite{subrahmanian2016darpa}.

\noindent\textbf{Causality.} This paper presents a set of causality metrics and unsupervised label propagation model to identify PSM accounts~\cite{shaabani2018detecting}. However, since our approach is supervised, we only use the causality metrics  and evaluate its performance in a supervised framework.

\noindent\textbf{C2DC.} This approach uses time decay causal community detection-based classification to detect PSM accounts~\cite{alvari2018early}. We also considered time decay causal metrics with random forest as another baseline method (TDCausality).

\noindent\textbf{Sentimetrix.} This approach is proposed by the top-ranked team in the DARPA Twitter Bot Challenge~\cite{subrahmanian2016darpa}. We consider all features that we could extract from our dataset. Our features include tweet syntax (average number of hashtags, average number of user mentions, average number of links, average number of special characters), tweet semantics (LDA topics), and user behaviour (tweet spread, tweet frequency, tweet repeats). The proposed method starts with a small seed set and propagate the labels. As we have enough labeled dataset for the training set, we use  random forest as the learning approach.

We use dataset $\mathcal{A}$ to evaluate different approaches. Fig.~\ref{fig:sup_bl_roc} shows the precision-recall curve for these methods. As it is shown, in the supervised framework, \textit{Sentimetrix} outperforms all approaches in general. Also, \textit{Causality} is a comparable approach with Sentimetrix with the constraint that the precision is no less than 0.9 as illustrated in Fig.~\ref{fig:sup_bl_prrc}.
Note that, most of the features used in the previous bot detection work take advantage of  content and network structure of  users. However, this is not the case in our proposed metrics and approach.

\begin{figure}[t!]
	\centering
	\begin{subfigure}[b]{.5\columnwidth}
		\centering
		\includegraphics[width=1\columnwidth]{sup_bl_roc.png}
		\caption{ROC curve}
		\label{fig:sup_bl_roc}
	\end{subfigure}%
	\begin{subfigure}[b]{.5\columnwidth}
		\centering
		\includegraphics[width=1\columnwidth]{sup_bl_prrc.png}
		\caption{Precision-Recall curve}
		\label{fig:sup_bl_prrc}
	\end{subfigure}%
	\caption{Performance of the baseline methods on dataset $\mathcal{A}$}
	\label{fig:dist_casc}
\end{figure}

\subsection{Supervised Learning Approach}
 In this section, we describe the classification results using proposed metrics with different learning approaches. We used both datasets for this experiment. First, we use the same dataset as we used in baseline experiments ($\mathcal{A}$). Then, we use dataset $\mathcal{B}$ for comparing top methods which is 8.5 times larger than dataset $\mathcal{A}$.

 Fig.~\ref{fig:sup_mt_roc} shows the ROC curve for different approaches. As it is shown, deep neural network achieved the highest area under the curve. Note that, the deep neural network is comparable with random forest as it is shown in Fig.~\ref{fig:sup_mt_prrc} on Dataset $\mathcal{A}$. The proposed approaches could improve the recall from 0.22 to 0.49 with the precision of 0.9. According to the random forest, top features are from all categories including causality metrics: $\epsilon_{nb}$, $\epsilon_{wnb}$, user-message graph-based metrics: user's PageRank $PR(u)$, median of retweeted message's PageRank $PS_{u,med}$, degree $D_u$, mean of verified users in his messages $U^vS_{u,mean}$, $\mathcal{T} S_{u,med}$, $\mathcal{T} S_{u,mean}$, median of length of the cascades $CS_{u,med}$, user graph-based metrics: weighted co-occurrence score $\mathcal{CO}^w_{u,N_u}$.

\begin{figure}[t!]
	\centering
	\begin{subfigure}[b]{.5\columnwidth}
		\centering
		\includegraphics[width=1\columnwidth]{sup_mt_roc.png}
		\caption{ROC curve}
		\label{fig:sup_mt_roc}
	\end{subfigure}%
\begin{subfigure}[b]{.5\columnwidth}
		\centering
		\includegraphics[width=1\columnwidth]{sup_mt_prrc.png}
		\caption{Precision-Recall curve}
		\label{fig:sup_mt_prrc}
	\end{subfigure}%
	\caption{Performance of different supervised approaches using proposed metrics on dataset $\mathcal{A}$}
	\label{fig:dist_casc}
\end{figure}

In Fig.~\ref{fig:sup_mt_2_prrc}, we probe the performance of the top two supervised approaches on larger dataset $\mathcal{B}$. Deep neural network is able to achieve the recall of 0.48 with the precision of 0.9. It is also able to achieve  AUC of 0.83 on this dataset (Fig.~\ref{fig:sup_mt_2_roc}). It is worth to mention that  we assigned higher weights to PSM accounts to deal with data imbalance problem.

\begin{figure}[t!]
	\centering
	\begin{subfigure}[b]{.5\columnwidth}
		\centering
		\includegraphics[width=1\columnwidth]{sup_mt_2_roc.png}
		\caption{ROC curve}
		\label{fig:sup_mt_2_roc}
	\end{subfigure}%
	\begin{subfigure}[b]{.5\columnwidth}
		\centering
		\includegraphics[width=1\columnwidth]{sup_mt_2_prrc.png}
		\caption{Precision-Recall curve}
		\label{fig:sup_mt_2_prrc}
	\end{subfigure}%
	\caption{Performance of the top two supervised approaches using proposed metrics on dataset $\mathcal{B}$}
	\label{fig:dist_casc}
\end{figure}

\subsection{Self-Training Semi-Supervised Learning Approach}
In this experiment, we randomly select 300 PSM and 300  normal users from dataset $\mathcal{B}$ for training and development sets and the rest of the dataset was considered as unlabeled data. We conduct two types of experiments:

\noindent\textbf{\textsc{WSeT} Algorithm.} In this experiment, we evaluate the self-training semi-supervised approach using Algorithm~\ref{alg:WSeT}. In this approach, we iteratively update the training set and the termination condition is accuracy of the model on the development set. We set the parameters as $\theta_{pr}=1$, $\alpha=0.05$, $\theta_{tr}=0.03$. We use a random forest classifier to train the model. The cumulative number of true positive and false positive is shown in Fig.~\ref{fig:ss_pos}. With using 300 PSM accounts as seed set, \textsc{WSeT} can find 29,440 PSM accounts with the precision of 0.81. Note that, we can stop the algorithm earlier.

In this case, precision varies from $0.97$ to $0.81$. Fig.~\ref{fig:ss_neg} illustrates cumulative number of selected users as normal users by \textsc{WSeT}. As shown, the number of true negatives (selected normal accounts as normal users) is 18,343 with precision of 0.93.
\begin{figure}[t!]
	\centering
	\begin{subfigure}[b]{.5\columnwidth}
		\centering
		\includegraphics[width=1\columnwidth]{ss_pos.png}
		\caption{PSM users}
		\label{fig:ss_pos}
	\end{subfigure}%
	\begin{subfigure}[b]{.5\columnwidth}
		\centering
		\includegraphics[width=1\columnwidth]{ss_neg.png}
		\caption{Normal users}
		\label{fig:ss_neg}
	\end{subfigure}%
	\caption{Cumulative number of selected users using \textsc{WSeT} Algorithm on dataset $\mathcal{B}$}
	\label{fig:dist_casc}
\end{figure}

\noindent\textbf{Supervised \textsc{WSeT} Algorithm.} In previous experiment, we assume that the supervisor checks accounts labeled as PSM by \textsc{WSeT} at the end. However, this process can be done iteratively. Here, we assume that the supervisor evaluates the \textit{PSM labeled accounts by \textsc{WSeT}} in each iteration and verify if they are either true or false positive. Therefore, these labels along with the non-PSM labeled accounts by \textsc{WSeT} are fed into \textsc{WSeT}. According to our results, the number of true positive increases to 80,652 with the precision of more than 0.8. That is, using this approach we can increase the number of true positive PSM accounts 2.7 times.

\begin{figure}
	\centering
	\includegraphics[width=.7\columnwidth]{ss_act.png}
	\caption{Cumulative number of selected users as PSM accounts using supervised \textsc{WSeT} Algorithm on dataset $\mathcal{B}$}
	\label{fig:ss_act}
\end{figure}

\section{Related Work}\label{sec:rw}

In summary, majority of previous work was based on three fundamental assumptions which make them different from our work. First, \emph{the information of the network is known}~\cite{subrahmanian2016darpa,goyal2010learning,benignitweets,abokhodair2015dissecting}. This assumption may not hold in reality. Second, \emph{they are language-dependent} ~\cite{subrahmanian2016darpa,morstatter2016new}. Third, \emph{the majority of botnet detection algorithms focused on bots in general.} That is, they did only consider the bots \emph{equally}~\cite{morstatter2016new,dickerson2014using} while here, we identify PSM accounts that spread harmful viral information.

\noindent\textbf{Identifying PSM accounts.} Compared to previous PSM account detection work in \cite{shaabani2018detecting, alvari2018early}, we propose graph-based features and unlike the unsupervised learning approach in \cite{shaabani2018detecting}, we expand out both the supervised and semi-supervised learning methods. We also develop a deep neural network  and show  how our proposed approaches improve them significantly.

\noindent\textbf{Identifying Automatic Accounts.}

DARPA conducted the Twitter bot detection challenge to identify and eliminate influential bots~\cite{subrahmanian2016darpa}. Most of the previous work extracted different sets of features (tweet syntax, tweet semantics, temporal behavior, user profile, friends and network features) and conducted supervised or semi-supervised approaches~\cite{subrahmanian2016darpa,davis2016botornot,dickerson2014using}. However,  without using content, and network structure, they perform poorly. Also, some of the features such as tweet semantics depend on the language. It is yet a challenge to apply these features to other languages such as Arabic.

\noindent{\textbf{Analysis of Terrorist Groups and Detection of Water Armies.}} Terrorist groups use social media for propaganda dissemination~\cite{al2015examining}. Benigni et al.~\cite{benignitweets} conducted vertex clustering and classification to find Islamic Jihad Supporting Community on Twitter. Abdokhodair et al. \cite{abokhodair2015dissecting} studied the behaviors and characteristics of Syrian social botnet. Chen et al.~\cite{chen2013battling} found that within the context of news report comments, user-specific measurements can distinguish water army from normal users. Our work is different from them since these methods  used features related to the accounts and network.

\section{Conclusion}
In this paper, we conducted a data-driven study on the pathogenic social media accounts. We proposed supervised and semi-supervised frameworks to detect these users. We achieved the precision of 0.9 with F1 score of 0.63 using supervised framework. In semi-supervised framework, we are able to detect more than 29K PSM users by using only 600 labeled data for training and development sets with the precision of 0.81. Our approaches identify these users without using network structure, cascade path information, content and users' information. We believe our technique can be applied in the areas such as detection of water armies and fake news campaigns. Our future plan is to combine the proposed semi-supervised approach with unsupervised one  in~\cite{shaabani2018detecting} in order to find the initial seed set for the semi-supervised approach.\medskip

{\small
	\noindent\textbf{Acknowledgments.} Some of the authors are supported through the ARO (grant  W911NF-15-1-0282).

\bibliographystyle{IEEEtran}
{

redpajama_set_name: RedPajamaArXiv


Resolution of the Cabinet of Ministers "On record keeping concerning citizens of the Republic of Uzbekistan employed abroad" was adopted in Uzbekistan in the middle of May 2007. Problems of labor immigration are in the focus of interest in Uzbekistan because labor immigration is a distinct prospect for a great many of its citizens. It is only logical therefore that the document in question and its practical implementation attract vivid interest.
Official Uzbek media outlets are notoriously closemouthed with regard to the authorities' intentions, and Ferghana.Ru editorial office is therefore bombarded with questions that mostly come down to the following. Do the latest standard acts check with international conventions on labor immigration? Must citizens of Uzbekistan apply to the national authorities for work permits abroad? Is the absence of these work permits punishable by the acting Uzbek legislation? Will the recent resolution make departure for foreign countries (obtaining of exit visas) more difficult? Ferghana.Ru correspondents approached independent experts for answers to these and other questions.
Experts are unanimous that the Uzbek authorities cannot be angling for any restrictions in connection with labor immigration because immigrants' transactions to Uzbekistan account for up to 8% of the GDP (official estimates gauge them to reach $1.3 billion every year). Labor immigrants support themselves and their families. Every labor immigrant supports 4-5 dependants at home, at least one of them an adult without a stable job and therefore stable income. One million labor immigrants from Uzbekistan mean that the national budget can save $40 million worth of unemployment reliefs and nearly $25 million worth of grants to poor families every year. Moreover, what transactions Uzbek families receive from abroad support the domestic demand and, to some extent, domestic production. Last but not the least, some labor immigrants do succeed and save enough to launch their own businesses in Uzbekistan and that means new jobs for the locals.
Experts estimate the number of labor immigrants from Uzbekistan (legitimate and illegal) at 1-1.5 million and the figure increases by 5-8% every year. Tong Jahonim, the international organization that researched labor immigration between 2003 and 2006, claims that three regions of the Ferghana Valley alone provided up to 250,000 immigrants every year even though the authorities withheld the permission to leave the country to almost 90,000 potential labor immigrants in 2005 because of the tragic events in Andijan.
%Colloquially known as Gastarbeiters, labor immigrants find employment for a season or for several years in the majority of countries they end up in. Kazakhstan is the only exception. Seasonal migrants go to its southern regions to harvest cotton every year by whole families. Between 200,000 and 300,000 Gastarbeiters from Uzbekistan work the Kazakh cotton plantations in the harvesting season, and some of them later remain as construction workers.
In Russia, Uzbek Gastarbeiters mostly concentrate in the Moscow region, St.Petersburg, Samara, Saratov, and South Siberia.
Some specialists say that labor immigration from Uzbekistan to South Korea ebbed after a series of scandals in 2004. Official Seoul stiffened immigration control and visa procedures when it was discovered that Uzbek girls were mostly working in South Korea as prostitutes.
Mass labor immigration has both pros and cons. The latter include the downfall of Uzbek families' traditional values and breakup of homes because of conflicts with elders or because women in these families find themselves without the provider are compelled to seek other sources of income.
Secondly, mass depopulation of whole regions (do not forget that it is the economically active population that is leaving) makes their own economic revival and restoration of the local markets of labor impossible. Practically all able-bodied population leaves the Yakkabag district of the Kashkandarja region, Syrdarja district of the Jizak region, Arkhangelsk district of the Tashkent region, and districts of the Syrhandarja region and Karakalpakstan.
Uzbek Gastarbeiters regularly encounter cheating employers, violence, and other forms of encroachment on their rights. Every tenth runs the real risk of finding him- or herself a slave, every second is mercilessly exploited. More than 85% of them work 60 hours a week for a pay 2.5 times smaller than what is paid the locals. According to what information Tong Jahonim has compiled, 67.2% of Gastarbeiters fell victims of exploitation, 0.2% of them victims of sexual abuse. Ninety-eight percent of labor immigrants admit that they have been swindled at least once.
Last but not the least, mass labor immigration has a negative effect on the youths who do not care for professional or higher education since that is not what a Gastarbeiter needs. As a matter of fact, immigration even affects the socially prosperous strata of the youth. Tong Jahonim approached a lot of students in Tashkent in 2003 and discovered that 92.5% of them aged 16-21 would leave the country in search of employment abroad given half a chance. Only 0.06% of them in the meantime displayed at least a dim knowledge of the legal procedures involved or of foreign immigration legislations.
Its sovereignty gained 16 years ago, Uzbekistan is not a signatory to UN conventions on labor immigration. It is not even a member of the World Labor Organization. No labor immigration accords with CIS neighbors were signed until recently.
On June 28, 2007, the government of the Russian Federation received three draft accords with the Republic of Uzbekistan: on cooperation in the war on illegal immigration, on labor and protection of labor immigrants' rights, and on readmission (expulsion of illegal immigrants). The document on illegal immigration regulates matters of border and immigration control and stipulates record keeping on trespassers, exchange of information, joint research, and personnel training for control structures. The measures stipulates by the latest resolution of the Uzbek Cabinet of Ministers will hopefully be taken simultaneously with the signing and ratification of bilateral accords between Uzbekistan and Russia.
Right before the middle of the 1990's, Uzbek legislation pertaining labor immigration was restricted to Article 13 of the law "On employment". It stated that "Citizens of the Republic of Uzbekistan are empowered to work and seek employment beyond the territory of the Republic of Uzbekistan, their labor activity there and labor activity of foreigners in the Republic of Uzbekistan regulated by the laws of the Republic of Uzbekistan." The "laws" in question took the form of government resolutions dated 1995, 2001, and 2003.
Government resolution "On organization of labor activity of citizens of the Republic of Uzbekistan abroad" (2003) directed would-be labor immigrants to a special department of the Labor and Social Protection Ministry's Agency for External Labor Immigration for permits to seek employment abroad. The Agency in the meantime was expected to run its decisions by the Interdepartmental Commission of the Cabinet of Ministers. In other words, the question of what citizens of Uzbekistan were to be permitted to become legitimate labor immigrants was handled at the government level. Independent experts appraise it as a bad practice because for 20,000 applications to the Agency every year only 1,500 or so permits were issued, and only for menial work at that.
Even the application procedure itself is something. The applicant is expected to provide a copy of the contract (translated into the state language with the notary public's seal and stamped by the Uzbek consulate in the given foreign country) specifying all social guarantees and medical insurance terms and, no less importantly, the employer's obligation to help the employee with the local work permit. Sure, immigration legislations of a great deal of other countries involve an even more complicated redtape that is supposed to be defending labor immigrants' rights. On the other hand, the ratio of legitimate labor immigrants to labor immigration that exceeds 800,000 even by official estimates plainly shows absolute deficiency of this practice in the Commonwealth and specifically in Uzbekistan.
The Agency for External Labor Immigration mostly sent labor immigrants to South Korea (more than 90%). All necessary paper work was usually done by lawyers from intermediator firms. Also importantly, Uzbek Gastarbeiters went to foreign countries as apprentices, the nuance that permitted the host countries not to apply local legislation to them (to extend work hours and to underpay). In fact, even whoever departed Uzbekistan on the official Agency quotas were regarded as apprentices. Mass labor immigration remained mostly illegal - even from the standpoint of Uzbek legislation.
Clause 6 of the latest Cabinet resolution instructs the Labor and Social Protection Ministry and Economy Ministry with all involved ministries and departments to come up with simpler procedures and smaller fees for the permits to work abroad and gives them two months for it. In the meantime, would-be labor immigrants are unlikely to stampede to the Agency for External Labor Immigration for permits unless the "involved ministries and departments" design some measures of coercion.
As things stand, acting Uzbek legislation does not stipulate any serious punishment for employment abroad without official permission from the authorities of Uzbekistan (save for involvement with illegal armed formations, of course, which is punishable by between 5 and 10 years imprisonment under Article 154 of the Criminal Code). Experts point out, however, that the relations between the Uzbekistan powers-that-be and citizens are such nowadays that the former may actually come up with some sort of administrative prosecution of illegal labor immigrants. Say, for tax evasion or concealment of income (Article 174), violation of business regulations (Article 176), or improper documents for travel abroad (Article 226) - all of them punishable by a fine amounting to three, five, or ten minimal wages.
In the meantime, measures like that are unlikely to become mass applied in a hurry because the authorities, informed as they are on labor immigrants, certainly wield the administrative resource to bring every deliberate or inadvertent violator to answer. It is the people who do fine abroad, so fine that they may attract the attention of Uzbek state structures, who should be - and are - apprehensive. Unfortunately, stiffer state control over labor immigrants and their income paves way to corruption and the Uzbek authorities are already unbelievably corrupt.
Some experts suspect - not unreasonably - that the intended record keeping with regard to Gastarbeiters will allow for new forms of putting under pressure the "politically disloyal" and their families. Of course, it depends to a considerable extent on the general political and civil atmosphere in Uzbek society.
The May resolution specifies no changes in the exit visa procedures. These days, whoever applies for the exit visa is only expected to forward to the local OVIR (department of visas and passports) his or her biography, data on relatives, political preferences, troubles with the law (if any), past jobs, and indicate the purpose of the intended visit. It is common knowledge in the meantime that regardless of the true purpose of the intended trip abroad, people applying for exit visas claim to be ordinary tourists intent on visiting Turkey, China, or Southeast Asia (it became widespread practice in the early 1990's when a lot of Uzbeks became shuttle vendors buying cheap commodities abroad and bringing them to Uzbekistan). The information thus provided to the OVIR should be certified at the latest job. When, however, it is an unemployed who applies for the exit visa, the information may be authenticated by the mahallja committee which is never a problem particularly when the applicant is prepared to donate a small sum to the mahallja. It is never a bribe, no, just a "demonstration of good will" with regard to the local self-government body that is ever underfinanced and does need donations. When the centralized database on labor immigrants is finally formed, the OVIRs will be able to use it too even though there is actually nothing to prevent applicants from indicating literally any purpose of the intended trip abroad, and exit visas in Uzbekistan are issued for two years. Visa-holder may actually decide to work a bit somewhere in the Commonwealth in order to save enough for a vacation in Thailand afterwards. Nothing criminal about it, is there? From the legal standpoint, however, absolutely every violation of the provisional residence or labor regulations logged by foreign immigration services and transmitted to their Uzbek analogs may provide an excuse for denial of the visa.
Traditionally enough, the Uzbek authorities ascribe the latest resolution of the Cabinet to the best intentions and refer to the experience in this sphere accumulated by advanced countries. Time will show what effect on the lives of labor immigrants the resolution and bilateral agreements with CIS countries are going to have. The population had better hope for the best - as always.


text:
Any Group Documentaries (8) Pathé newsreels (24) Pathetones (1) Selected originals (2) Unknown sel originals (3) Unissued / unused (24) Reuters - Including Visnews (10) Reuters - Source to be Verified (5) Reuters - Gaumont Graphic Newsreel (1) Reuters - Gaumont British Newsreel (1)
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India Today AKA In India To-Day (1930)
Mahatma Gandhi completes march to break salt laws. India.
India: Mahatma Gandhi Assassinated (1948)
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India: Indians Remember Mahatma Gandhi (1965)
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Eleanor Roosevelt visits India and lays wreath on Gandhi's memorial.
Bulganin In India (1955)
Spectacular welcome to Bulganin and Khruschev to Delhi - streets covered in...
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Gandhi arrives in London to attend the Round Table Conference.
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President Nixon in Delhi, India and Mildenhall in Suffolk.
India: Prime Minister Mrs. Indira Gandhi Pays Respects At Memorial To Mahatma... (1971)
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Funeral of Mahatma Gandhi. India.
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text:
Tag Archives: Juan Perón
Musical, Theatre
Review Evita, Wales Millennium Centre by Barbara Michaels
August 29, 2018 BarbaraMichaels	Leave a comment
The real-life story of Eva Perón is a classic case of fact being stranger than fiction, and couldn't be more suited to adaptation as a musical, and a highly successful musical at that ever since Andrew Lloyd Webber and Tim Rice's hit production in London's West End back in 1978, followed by Broadway a year later and since then a string of professional productions worldwide.
Eva Duarte was living in poverty in rural Argentina in the late 1930s when her involvement with a musician takes her to Buenos Aires where her determination to become a star eventually results in a meeting with Argentinian Colonel Juan Perón at a benefit concert. He too has an ambition – in his case to become President of Argentina. With Eva – who later becomes known as Evita – by his side he succeeds. Meanwhile, despite her unflagging work for the poor of the country, Eva's extravagant lifestyle leads to criticism.
This new production by the really Useful Group under the banner of Bill Kenright breathes new life into the show with a brand new cast including the charismatic Glenn Carter as Che. Acting as narrator, Carter's expressive delivery and fine voice guide the audience through the twists and turns of the story of the ambitious girl from the sticks who becomes the wife of the President of Argentina, with all the trappings of wealth and status that go with it.
Taking on the role of Eva is Lucy O'Byrne – not an easy task, given that not one but two showbiz icons – Elaine Paige in the Seventies West End production and Madonna in the film – have previous in this respect. O'Byrne's voice is strong but she needs to guard against a resulting loss of clarity at times, which is shame given the emotive quality of Tim Rice's wonderful lyrics. O'Byrne came into her own in the second half with her performance of what was to be Eva Perón's last appearance and her singing of 'Don't Cry For Me, Argentina' – heart-rending in its beauty. Interpreted by, and under the baton of musical director Anthony Gabriele, Andrew Lloyd Webber's marvellous musical score is given full throttle, at times to the extent of being overloud in the first half but meltingly moving in the highly charged and emotional second half.
Good to see some of the talent that comes out of Wales in Swansea-born Mike Sterling as Perón. Historically in the musical the role is underplayed in relation to that of Eva. Accordingly, Sterling gives us only a glimpse of the man that was Perón leaving us aware that behind a pragmatic exterior lies an ability to recognise and rely on the power behind the throne – Eva.
Important to the first half of the story is Magaldi, the musician whose eye for the girls is Eva's route to Buenos Aires. The dark good looks of Oscar Balmaseda make for a neat bit of casting, as does that of Cristina Hoey as Perón's former mistress, swiftly given the boot by Eva. Although Hoey makes only one appearance, and a brief one at that, her singing of Another Suitcase, In Another Hall is up there with the best. This girl is definitely one to watch.
At the end of the day it is, as with much if not most musical theatre, the story plus the songs that make or break the show, and here the plot is a given and as for the songs – beautiful.
Runs until Saturday 8 September 2018
Andrew Lloyd WebberAnthony GabrieleBill KenrightCardiffChristina HoeyEva PeronEvitaGlenn CarterJuan PerónLucy O'ByrneMike SterlingOscar BalmasedaWales Millennium Centre


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AP To Build Major New Studio in Central London
Sunday, April 4, 2010 - 11:43 am
AP Global Media Services, the video newsgathering facilities arm of The Associated Press, has announced that it will build a major new studio and production space in the heart of central London.
The broadcast facility will comprise four HD compatible studios, two of which will be multi-camera. The studios will overlook many of central London's most famous and recognizable landmarks. Located in Haymarket's New Zealand House, the studios will also be within walking distance of Leicester Square's red-carpet events, Whitehall and 10 Downing Street.
As well as providing breathtaking views for broadcasters and production companies needing London backdrops, the studios will also provide editing facilities and high-speed fibre transmission links to the AP's global satellite and fibre network.
"These studios represent a huge investment and vote of confidence in the industry by the AP and highlight its commitment to providing the best facilities for its clients," says Mr. Alla Salehian, director of AP Global Media Services. "We believe the studios will quickly become the premier live shot facility in London. Their location and elevation above the London skyline will give clients multiple live backdrops and camera angles. There's nothing like it and broadcasters and the viewing public will be treated to vistas of central London they have never seen before."
The studios will be fully-operational in time for the United Kingdom general elections anticipated in early-May 2010. The facility will be able to transmit both HD and SD live video signals to newsrooms worldwide, as well as service the production requirements of clients needing both video editing facilities and immediate access to AP's video news, entertainment, and sports news content.
"Our plans for broadcaster support include all major London events in the coming years – including the 2012 Olympic Games, movie premieres, cultural events, and, possibly, even a Royal wedding or two," says Salehian. "We're confident these studios will become the UK base for British and international broadcaster and production companies who want to produce and anchor regular strands or one-offs programmes from London. Furthermore, it's ideal for film and entertainment companies and PR firms organising celebrity press junkets, as it's just a stone's throw from Leicester Square."
AP is working in partnership with the studio design and construction company WTS Broadcast, who will build and maintain the studios, whilst AP will handle all client bookings and enquiries.
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