text:
\section{Introduction}\label{sec:intro}
The prevalence of fake news\footnote{Here we use the definition in~\cite{zhou_fake_2018}: \textit{fake news is intentionally and verifiably false news published by a news outlet}.} on social media has serious repercussions on our society. Especially when equipped with big data analysis, it can accurately reach specific target audiences to spread fear, aggravate hatred, and cause riots and violence.

While significant efforts have been made on fake news detection, most existing work focuses on the \textit{textual content} and \textit{social context}, where \textit{textual content} includes the news headline and body, and \textit{social context} means user interactions over social media. For example, once a news item is published online, it may be tweeted by multiple users. Each of these tweets and its retweets form a separate cascade~\cite{vosoughi_spread_2018}, and all the cascades form the propagation pattern/network (we use these two terms interchangeably in this work) of a news item.

Knowledge-level information, on the other hand, has been much less explored. Here knowledge refers to the entities in the news content and the relations between them. Typically, it can be represented in the format of a triple: (Subject, Predicate, Object), \emph{i.e.,}\xspace SPO triple~\cite{noauthor_resource_2017}. Within the limited work on knowledge-based fake news detection, an external knowledge graph is often required. For example, Cui \emph{et~al.}\xspace~\cite{cui_deterrent_2020} incorporate an article-entity bipartite graph and a medical knowledge graph to better capture the important entities and guide the embedding of news articles to detect healthcare misinformation. However, it is unlikely for any knowledge graph to contain all possible entities and relations, and it is not a trivial task to accurately predict missing ones. To overcome this issue, in this work, we investigate \textbf{knowledge-based fake news detection that does not require any external knowledge graph.}

\begin{figure*}[ht!]
\centering
\begin{subfigure}{.75\textwidth}
  \centering
  \includegraphics[width=\textwidth]{knowledge_based.pdf}
  \caption{Knowledge-based fake news detection. Information is extracted from all news items to form a single knowledge graph, where each news item can be represented by a subgraph, so that the fake news detection problem becomes a subgraph classification task. A SubGNN model is then trained to classify each subgraph/news item.}
  \label{figure_overview_knowledge}
\end{subfigure}
\begin{subfigure}{0.75\textwidth}
  \centering
  \includegraphics[width=.9\textwidth]{multi_modal.pdf}
  \caption{Multi-modal fake news detection that combines extracted knowledge, text content and propagation network. The propagation-, text- and knowledge-based models are pre-trained separately, and their generated embeddings are concatenated to train a MLP.}
  \label{figure_overview_multimodal}
\end{subfigure}
\caption{Overview of the proposed knowledge-based and multi-modal fake news detection algorithms.}
\label{figure_overview}
\end{figure*}

Pan \emph{et~al.}\xspace~\cite{pan_content_2018} approach this problem by constructing two knowledge graphs, with one on real news (\(KG_{T}\)) and one on fake news (\(KG_{F}\)). Each of these two knowledge graphs is then used to train a TransE~\cite{bordes_translating_2013} model, and a news item is verified by comparing the average or maximum bias of the two models over the extracted relations, where the bias of a triple \((s, p, o)\) is defined as \(||s+p-o||_{2}^{2}\). A news item is classified as ``Real" if the bias of the model trained on \(KG_{T}\) is smaller, and ``Fake" otherwise.

In our work, by contrast, we look at the problem from a different perspective: given the recent development on graph neural networks (GNNs) and their superior performance on non-Euclidean data, we investigate whether it is possible to transform fake news detection into a knowledge graph classification problem.

Initially, we test a method that constructs a separate knowledge graph for each news item, and then applies GNNs for graph-level classification. However, our preliminary experiments suggest that this method does not achieve satisfactory results, with an average accuracy only around 73\%. A key reason is that normally a very limited number of relations/triples can be extracted from each news item, which means that the constructed knowledge graph is too small to accurately verify the veracity of a news item.

Therefore, we propose a new approach and \textbf{transform the problem of fake news detection into a subgraph classification task}. Specifically, our contributions include:

\begin{itemize}[wide]
    \item Inspired by the work of~\cite{alsentzer_subgraph_2020}, which designs a GNN model called SubGNN for subgraph classification, we propose to extract entities and relations from each news item, all of which form a single knowledge graph where a news item is represented by a subgraph (Fig.~\ref{figure_overview_knowledge}), and then all subgraphs with their corresponding news labels (fake/real) are used to train a SubGNN model, so that the obtained model can classify each subgraph/news item. As demonstrated by the experimental results in Section~\ref{sec:experiment}, this method can achieve much better performance.

    \item To further improve the performance of the above model, we develop a simple but effective multi-modal fake news detection algorithm. In addition to extracted knowledge, other forms of information, such as the textual content and propagation network, can also contribute to detecting fake news. Specifically, as can be seen from Fig.~\ref{figure_overview_multimodal}, in our proposed method three models are first trained separately: (1) a propagation-based model~\cite{han_graph_2020} that verifies a news item purely on its propagation pattern; (2) a document classification model using hierarchical attention networks~\cite{yang_hierarchical_2016} that operates directly on the news content; and (3) a knowledge-based model proposed in this work. We demonstrate that by concatenating the generated embeddings of these three models to train a multilayer perceptron (MLP), this seemingly simple approach can outperform each individual model by a clear margin.
\end{itemize}

The remainder of this paper is organised as follows:
Section~\ref{sec:problem} defines the research problem;
Sections~\ref{sec:kg} and~\ref{sec:subgraph} present our knowledge-based fake news detection algorithm which does not require any external knowledge graph;
Section~\ref{sec:multimodal} introduces an architecture that combines knowledge-, text- and propagation-based models to facilitate multi-modal fake news detection;
Section~\ref{sec:experiment} provides experimental results to demonstrate the effectiveness of our proposed methods;
Section~\ref{sec:related} reviews previous work related to fake news detection on social media;
and finally Section~\ref{sec:conc} concludes the paper and offers directions for future work.

\section{Problem Definition}\label{sec:problem}
Originally, the fake news detection problem can be defined as: given a set of labelled news items \(\mathcal{D} = \{\left(W_{i}, y_{i}\right) |\ i=1,\ 2,\ ...\}\), where \(W_{i} \in \mathcal{W}\) is the textual content for news item \(i\) (\emph{i.e.,}\xspace a sequence of words), and \(y_{i} \in \mathcal{Y} = \{0\ \text{(Real)}, 1\ \text{(Fake)}\}\) is the label of \(W_{i}\), the goal is to learn a mapping \(g: \mathcal{W} \to \mathcal{Y}\) that classifies each news item.

In this work, we take a knowledge-based approach and break down the above formulation into the following two sub-problems:

\myparagraph{Knowledge graph construction.} The aim of the first step is to extract information from each news item to construct a knowledge graph. Conventionally, this involves named entity recognition and relation extraction, which have been extensively studied. However, we have tested several existing named entity-based relation extraction techniques, and our results suggest that when applied to news items, these methods normally generate a relatively small number of relations, which may lead to substantial information loss. Therefore, in the first sub-problem we design a new relation extraction algorithm that extracts a set of relations/triples \(R_{i} = \{(e_{ij}, r_{ij}, e_{ij}^{\prime}) | j = 1, 2, ...\}\) from news content \(W_{i}\), where \(e_{ij}, r_{ij}, e_{ij}^{\prime}\) contain one or multiple words in \(W_{i}\). Each of these triples means that \(e_{ij}\) and \(e_{ij}^{\prime}\) has the relation of \(r_{ij}\). For example, a triple (David Warner, troll, Virat Kohli) can be extracted from the sentence ``David Warner trolls Virat Kohli on Instagram over his grey beard" (Fig.~\ref{fig:example1}). Note that here we do not pre-define any relation type or named entity.

\myparagraph{Subgraph classification.} Once all the relations are extracted from news items, and a single knowledge graph is constructed (in the case where a part of the graph is isolated from the rest, we only keep the largest connected component), each news item is assigned to a subgraph based on its extracted relations. Therefore, the original fake news detection problem is transformed into a subgraph classification task formulated as follows: given a set of labelled sub-knowledge graphs \(\{\left(SG_{i}, y_{i}\right) |\ i=1,\ 2,\ ...\}\), where \(SG_{i} \in \mathcal{SG}\) represents the sub-knowledge graph that corresponds to news item \(i\), and \(y_{i} \in \mathcal{Y} = \{0\ \text{(Real)}, 1\ \text{(Fake)}\}\) is the label of \(SG_{i}\), then the goal is to learn a classifier \(f: \mathcal{SG} \to \mathcal{Y}\) that labels each subgraph. Note that here different subgraphs are not necessarily mutually exclusive and may contain common nodes.

In the following two sections, we explain in detail our solution to the above two problems.

\section{Knowledge Graph Construction}\label{sec:kg}
In order to extract relations from news items to build a knowledge graph, we have first tested the following options: (1) an OpenNRE~\cite{han_opennre_2019} model trained on the Wiki80 dataset\footnote{https://github.com/thunlp/OpenNRE/blob/master/benchmark/download\_wiki80.sh} with a BERT encoder, (2) an OpenNRE model trained on the Wiki80 dataset with a CNN encoder, (3) an OpenNRE model trained on the TACRED dataset\footnote{https://nlp.stanford.edu/projects/tacred/}~\cite{zhang_position-aware_2017} with a BERT encoder, (4) an ATLOP~\cite{zhou_document-level_2021} model trained on the DocRED dataset~\cite{yao_docred_2019}. The first three models are for sentence-level relation extraction, while the last one is for document-level relation extraction---hence it can extract more accurate relations than the other three models in our case.

Our experimental results suggest that all these models extract a relatively small number of relations from each news item, which may lead to substantial information loss. In addition, it is also unlikely for all the extracted relations to form a single graph. A main reason is that the relation types are pre-defined in these three datasets: Wiki80/TACRED/DocRED contains 80/41/96 relations, which cannot cover all the scenarios. As a result, in many cases even though a certain type of relation does exist, ``\textit{no\_relation}" is returned instead.

\begin{algorithm}[t]
 \LinesNumbered

 \SetKwInput{Input}{Input}
 \SetKwInput{Output}{Output}
 \Input{The textual content $W=\{w_0, w_1, ...\}$ of a news record}
 \Output{The list of relations $L$}
 $L \leftarrow \emptyset$;\\
 $W \leftarrow coreference\_resolution(W)$;\\
 $\{D_0, D_1, ...\} \leftarrow dependency\_parsing(W)$;\\
 $\{p_0, p_1, ...\} \leftarrow pos\_tagging(W)$;\\
 $\{l_0, l_1, ...\} \leftarrow lemmatization(W)$;\\

\For{$i$ in $\{0,1,2,....,|W|-1\}$}{
    \If{$p_i$ is a verb}{
        $\{left\_nodes , right\_nodes\} \leftarrow \{\emptyset, \emptyset\}$;\\
        $edge\_type \leftarrow l_i$;\\
        \For{$(w_k, r, w_i)$ in $D_i$}{
            \If{$l_k$ is ``not"}{$edge\_type \leftarrow ``not\_" + edge\_type$;}
            \ElseIf{$r$ in $\{nsubj\}$}{$left\_nodes \leftarrow left\_nodes \cup \{l_k\}$;}
            \ElseIf{$r$ in $\{dobj, iobj, attr, xcomp\}$}{$right\_nodes \leftarrow right\_nodes \cup \{l_k\}$;}
        }
        \For{$left\_node$ in $left\_nodes$}{
            \For{$right\_node$ in $right\_nodes$}{
                $L \leftarrow L \cup {(left\_node, edge\_type, right\_node)}$;\\
            }
        }
    }
}
 $return\text{ }L$
\caption{Relation Extraction}
\label{algo:relation_extraction}
\end{algorithm}

\begin{figure*}[t!]
    \centering
    \includegraphics[width=.6\textwidth]{example1.pdf}
    \caption{An illustration of Algorithm~\ref{algo:relation_extraction}.}
    \label{fig:example1}
\end{figure*}

One option is to create our own dataset from the collected news items with a larger number of pre-defined relations, and then retrain an OpenNRE or ATLOP model. However, in order to significantly increase the number of pre-defined relations, we also need a considerably larger number of relation instances for training. Considering that DocRED already has a total number of \(1,508,320\) instances, it is unlikely to obtain a much larger quantity from the thousands of collected news items.

Therefore, we design a new technique that can expand the number of extracted relations. Specifically, we do not pre-define any relation type, and instead focus on the verbs in each sentence, since verbs often play an important role in deciding the veracity of a statement. Algorithm~\ref{algo:relation_extraction} summarises our relation extraction method. As illustrated in Fig.~\ref{fig:example1}, for a news record,

\begin{itemize
    \item The title and the body text are concatenated to extract its textual content $W=\{w_0, w_1, ...\}$.
    \item Co-reference resolution\footnote{\href{https://spacy.io/universe/project/neuralcoref}{https://spacy.io/universe/project/neuralcoref}} is performed to replace all the mentions in $W$ that refer to the same real-world entity with a single token (Line 2). In the given example, ``Kohli" is replaced by ``Virat Kohli".
    \item The grammatical structure of each sentence in $W$ is extracted using the dependency parser available in Spacy\footnote{\href{https://spacy.io/usage/linguistic-features\#dependency-parse}{https://spacy.io/usage/linguistic-features\#dependency-parse}} (Line 3). This step returns a set of tuples $D_i$ for each word $w_i$. Each entry in $D_i$ is a tuple $(w_k, r, w_i)$, where $w_k$ is another word in the same sentence of $w_i$ that is related to word $w_i$ from the relation type $r$, \emph{e.g.},\xspace nominal subject ($nsubj$), direct object ($dobj$), open clausal complement ($xcomp$), as shown in the third box in Fig.~\ref{fig:example1}. Note that the relation here has different meaning from the relation between entities mentioned above in the problem definition.
    \item The Part-of-Speech (POS) tags $\{p_0, p_1, ...\}$ and the base-forms $\{l_0, l_1, ...\}$ of the words in $W$ are recovered using the POS tagger\footnote{\href{https://spacy.io/usage/linguistic-features\#pos-tagging}{https://spacy.io/usage/linguistic-features\#pos-tagging}} and the lemmatizer\footnote{\href{https://spacy.io/usage/linguistic-features\#lemmatization}{https://spacy.io/usage/linguistic-features\#lemmatization}} in Spacy (Line 4-5).
    \item The verbs in $W$ are identified by looping through POS tags of the words in $W$ (Line 6-7).
    \item For each identified verb $w_i$, the connected words in the dependency parse tree $D_i$ are analysed. If a negation, \emph{i.e.,}\xspace words that reverse the meaning of a word, is found to be attached to $w_i$, $w_i$ is updated as ``\textit{not\_}" $+\  w_i$, \emph{e.g.},\xspace \textit{``care"} becomes \textit{``not\_care"} in the given example (Line 11-12).
    \item Then, the connected nodes are categorised as either $left\_\allowbreak nodes$ or $right\_nodes$ based on their relation to $w_i$. If the relation type of a connected word is nominal subject ($nsubj$), it is added to $left\_nodes$ (Line 13-14).
    \item Otherwise, if the relation to $w_i$ belongs to one of the following types: direct object ($dobj$), indirect object ($iobj$), attribute ($attr$), and open clausal complement ($xcomp$), it is added to $right\_nodes$ (Line 15-16). In the example of Fig.~\ref{fig:example1}, for the verb ``troll", ``David Warner"/``Virat Kohli" is categorised as $left\_nodes$/$right\_nodes$, respectively. In addition, even though ``\textit{respond}" is a verb in the second sentence, it will be extracted as a possible object of ``\textit{not\_care}" by our algorithm due to the \textit{xcomp} connection between them.
    \item In the end, the relation set is constructed by connecting each item in $left\_nodes$ with each item in $right\_nodes$ using the corresponding verb, \emph{i.e.,}\xspace $w_i$ in the above example (Line 17-19).
\end{itemize}

\begin{comment}
\begin{figure}[t!]
    \centering
    \FramedBox{4.8cm}{0.9\columnwidth}{\textbf{H1N1 vaccinations to be offered to Guantanamo Bay detainees}
    \newline
    \newline
    The \textcolor{blue}{Pentagon} will \textcolor{blue}{offer} the H1N1 \textcolor{blue}{vaccination} to detainees at the U.S. facility at Guantanamo Bay, officials there said Friday.
    \newline
    \newline
    The \textcolor{blue}{Pentagon} \textcolor{blue}{made} the \textcolor{blue}{decision} based on U.S. government assessments that people held in detention facilities are at high risk for the pandemic, said Maj. Diana R. Haynie, a spokeswoman for Joint Task Force Guantanamo Bay, which is in charge of holding the suspected terrorists.
    }
    \FramedBox{0.5cm}{0.9\columnwidth}{\textbf{Subject:} Pentagon, \textbf{Object:} Vaccination, \textbf{Predicate:} Offer}
    \FramedBox{0.5cm}{0.9\columnwidth}{\textbf{Subject:} Pentagon, \textbf{Object:} Decision, \textbf{Predicate:} Make}
    \caption{An example}
    \label{fig:example1}
\end{figure}
\end{comment}

Our experimental results show that by applying the above approach, substantially more (over 10 times) number of relations can be extracted from the news items in total. For example, as shown in Fig.~\ref{fig:example2}, in the left text box is the snippet of a news item\footnote{https://www.politifact.com/factchecks/2010/feb/04/paul-krugman/krugman-calls-senate-health-care-bill-similar-law-/}, and the relations extracted by our method are listed in the middle text box. As a comparison, the relations extracted by the ATLOP model are given on the right.

\begin{figure*}[t!]
    \centering
    \includegraphics[width=.85\textwidth]{example2.pdf}
    \caption{An example that compares the extracted relations using Algorithm~\ref{algo:relation_extraction} and by an ATLOP model. Left text box: the snippet of a news item. Middle text box: relations extracted by Algorithm~\ref{algo:relation_extraction}. Right text box: relations extracted by an ATLOP model trained on the dataset of DocRED.}
    \label{fig:example2}
\end{figure*}

We have compared the cases where the knowledge graph is built using the relations extracted by (1) Algorithm~\ref{algo:relation_extraction}, (2) an ATLOP model trained on the DocRED dataset (since DocRED is especially collected for document-level relation extraction), (3) both (1) and (2). Our experiment suggests that the best result is achieved when only the relations extracted by Algorithm~\ref{algo:relation_extraction} are used.

Note that we are not proposing a general method for relation extraction, since it is unlikely for the defined rules to work for all cases, and Algorithm~\ref{algo:relation_extraction} is only for our knowledge-based fake news detection technique.

After all the relations are extracted from news items, we add them one by one to form a single knowledge graph. Then each news item can be represented by a subgraph, based on the relations extracted from it. Therefore, the original problem of detecting fake news now transforms into classifying each of the subgraphs, as explained in the next section.

\section{Subgraph Classification}\label{sec:subgraph}
In this section, we first give a brief introduction to graph neural networks and SubGNN, and then explain how a sub-knowledge graph is classified in our case.

\subsection{Background on Graph Neural Networks}\label{sec:background_gnn}
Given a graph \(\mathcal{G} = \allowbreak \mathcal{(V, E)}\), with vertex/node set \(\mathcal{V}\), edge set \(\mathcal{E}\), and node feature set \(\mathcal{X} \in \mathbb{R}^{|\mathcal{V}|\times d}\) (\emph{i.e.,}\xspace each node has \(d\) features), many GNN models can be formulated as a \textit{message passing framework}~\cite{battaglia_relational_2018,wu_comprehensive_2019} in which information is propagated from one node to another along edges.

During each message-passing iteration \(k\), the embedding for node \(v \in \mathcal{V}\) is updated according to the information aggregated from \(v\)'s neighbourhood \(\mathcal{N}(v)\), which can be expressed as follows:

\(h_{v}^{(k+1)} = UPDATE \left( h_{v}^{(k)},\ AGGREGATE \left( \left\{ h_{u}^{(k)}\ |\ u \in \mathcal{N}(v)\right\} \right) \right)\),\\
where \(h_{v}^{0} = x_{v} \in \mathcal{X}\). The update step is often omitted by adding self-loops to the input graph, and the node embedding becomes:

\(h_{v}^{(k+1)} = AGGREGATE \left( \left\{h_{u}^{(k)}\ |\ u \in \mathcal{N}(v) \cup \left\{v\right\}\right\} \right)\).

Take a basic GNN for example~\cite{hamilton_graph_2020}, the model can be defined as: \(H^{(k+1)} = \sigma\left(\left(A+I\right) H^{(k)}M^{(k+1)}\right)\), where (1) \(H\) is the matrix of node embeddings/representations; (2) \(\sigma\) is a non-linear activation function, \emph{e.g.},\xspace the rectified linear unit (ReLU) function; (3) \(A \in \{0, 1\}^{|\mathcal{V}|\times |\mathcal{V}|}\) is the adjacency matrix: \(A_{i,j}=1\) if there is an edge from node \(i\) to node \(j\), and \(A_{i,j}=0\) otherwise; and (4) \(M\) is the weight matrix to be learned.

\subsection{Background on SubGNN}\label{sec:background_subgnn}
Most existing work on GNN focuses on node- and graph-level prediction tasks, while subgraphs are much less studied. To bridge this research gap, Alsentzer \emph{et~al.}\xspace~\cite{alsentzer_subgraph_2020} propose SubGNN that learns an embedding function \(\mathcal{F}: \mathcal{SG} \to \mathbb{R}^{d_{S}}\) to map each subgraph into a lower-dimensional representation.

Specifically, the embedding function \(\mathcal{F}\) captures features from three channels---\textit{position}, \textit{neighbourhood} and \textit{structure}, each of which has two subchannels---\textit{internal} and \textit{border}:

\begin{itemize
    \item \textit{Position}. The \textit{internal position} of subgraph \(SG_{i}\) is defined as the distance between \(SG_{i}\)'s components---\(SG_{i}\) may contain a single connected component or multiple isolated components. The \textit{border position} is defined as the distance between \(SG_{i}\) and rest of \(\mathcal{G}\).
    \item \textit{Neighborhood}. The \textit{internal neighborhood} and \textit{border neighborhood} are defined as the identity of \(SG_{i}\)'s internal nodes and border nodes, respectively, where border nodes refer to nodes within the \(k\)-hop neighborhood of internal nodes.
    \item \textit{Structure}. The \textit{internal structure} is defined as the internal connectivity of \(SG_{i}\), while the \textit{border structure} is defined by edges connecting \(SG_{i}\)'s internal nodes to the border neighborhood.
\end{itemize}

\subsection{Classify Sub-Knowledge Graphs}
Each of the above channels can be used separately or together. We have tested multiple SubGNN models with different combinations of the three channels, and find that models that rely only on the structure channel gives the best result. This is because in our case, the internal and border structures, \emph{i.e.,}\xspace how nodes are connected with each other within a subgraph and with the rest of the graph, are more informative for determining whether a news item is fake or not. Interestingly, the neighborhood channel does not perform as well as the other two, but this is consistent with the ablation study in the original work~\cite{alsentzer_subgraph_2020}.

In order to facilitate subgraph-level message passing for the structure channel, a number of connected components (each subgraph may contain single or multiple components) are randomly sampled via triangular random walks~\cite{boldi_arc-community_2012}---they are called anchor patches \(\mathcal{A} = \{A_{i}|i=1, 2, ..., n_{A}\}\). The embedding of a subgraph component (\(SC\)) is then represented in the following form:\\
\(h^{(k+1)}_{SC} = \sigma\left(M^{(k+1)} \cdot \left[h^{(k)}_{SC}; AGG \left( \left\{\gamma \left(SC, A_{i}\right) \cdot a_{i}|i=1, ..., n_{A} \right\} \right) \right] \right) \),\\
where \(\gamma\) is a pre-defined similarity function, \(a_{i}\) is the learned representation of \(A_{i}\), \(AGG\) is an aggregation function, \emph{e.g.},\xspace the sum operator, and \(M\) is a layer-wise trainable weight matrix.

If a subgraph contains multiple isolated connected components, its embedding is generated by concatenating the embeddings of all components.

\section{Knowledge Enhanced Multi-modal Fake News Detection}\label{sec:multimodal}
In addition to the extracted knowledge, the textual content itself and how the news item propagates through the social network also provide valuable information for detecting fake news. Therefore, in this section we study how to combine our knowledge-based approach with existing content- and propagation-based methods for more accurate detection.

Formally, given a set of labelled news items \(\{(SG_{i}, W_{i}, P_{i}, y_{i})\ |\ i=1,\ 2,\ ...\}\), where \(SG_{i} \in \mathcal{SG}\) is the sub-knowledge graph, \(W_{i} \in \mathcal{W}\) is the textual content, \(P_{i} \in \mathcal{P}\) is the propagation network, and \(y_{i} \in \mathcal{Y} = \{0\ \text{(Real)}, 1\ \text{(Fake)}\}\) is the label of the corresponding news, the goal is to learn a classifier \(f: \mathcal{SG} \times \mathcal{W} \times \mathcal{P} \to \mathcal{Y}\) that can label each news item.

As shown in Fig.~\ref{figure_overview_multimodal}, we first train three knowledge-, text- and propagation-based models separately on the same training dataset. Then for each training instance, we feed its \(SG_{i}, W_{i}, \text{and}\ P_{i}\) into the obtained models to generate three separate embeddings \(h_{i_{K}},\ h_{i_{T}}, \allowbreak h_{i_{P}}\), all of which are concatenated to form the final embedding \(h_{i} = h_{i_{K}}\oplus h_{i_{T}}\oplus h_{i_{P}}\). In the end, an MLP is trained on the set of embeddings \(\{h{_i}|i=1, 2, ...\}\).

Our experimental results in Section~\ref{sec:experiment} demonstrate that this seemingly simple method outperforms each individual model by a clear margin. Specifically, in this work we choose the following two as the text- and propagation-based models:

\begin{itemize}[wide
    \item A document classification model using hierarchical attention networks~\cite{yang_hierarchical_2016}. The overall architecture of this model consists of four components: (1) a word encoder where words are embedded with a GRU-based sequence encoder; (2) a word-level attention layer where the importance of a word is measured by its similarity with a word-level context vector, which is jointly learned during the training process; (3) a sentence encoder that is also based on bidirectional GRU; and (4) a sentence-level attention layer that calculate the weight of a sentence in a similar way to (2). The reason why we choose this model is that previous work has shown that it performs better than other text-based models for fake news detection~\cite{shu_defend_2019}, such as LIWC~\cite{pennebaker_development_2015}, text-CNN~\cite{kim_convolutional_2014}.
    \item A propagation-based algorithm~\cite{han_graph_2020} that applies the GNN model of DiffPool~\cite{ying_hierarchical_2018} (built on top of GraphSage~\cite{hamilton_inductive_2017}) to verify a news item purely on its propagation pattern (as explained in the introduction) and the features of the corresponding Twitter users, including (1) whether the user is verified, (2) the timestamp when the user was created, (3) the number of followers, (4) the number of friends, (5) the number of lists, (6) the number of favourites; (7) the number of statuses, (8) the timestamp of the tweet. The adjacency matrix corresponding to the propagation pattern of a news item and the node feature matrix are fed as input for graph-level classification. We choose this model due to its simplicity and efficiency.
\end{itemize}

Note that (1) although the experimental results in the next section suggest that our knowledge-based model works well together with the above two models, especially the propagation-based model, they can be replaced by other options too; (2) in addition to text content and propagation network, there are other useful forms of information as well, including user replies, images and external knowledge graphs, especially domain-specific knowledge graphs. We leave more sophisticated fusion techniques for future work.

\section{Experimental Verification}\label{sec:experiment}
In this section, we empirically verify the effectiveness of our proposed knowledge-based and multi-modal fake news detection algorithms over two datasets with thousands of labelled news items.

\subsection{Datasets}
While there are a number of public datasets on fake news detection covering different domains, we choose the dataset of FakeNewsNet~\cite{shu_fakenewsnet_2018} in our work. FakeNewsNet contains labelled news from two websites: politifact.com\footnote{https://www.politifact.com/} and gossipcop.com\footnote{https://www.gossipcop.com/}---we call them PolitiFact and GossipCop hereafter. For each news item, the dataset provides both linguistic and visual information, all the tweets and retweets, as well as the information of the corresponding Twitter users. For more details please refer to~\cite{shu_fakenewsnet_2018}.

The reasons why we choose PolitiFact and GossipCop over other options include: (1) they align with the our definition of fake news---fake news is intentionally and verifiably false news published by a news outlet. Some public datasets on fake news detection, \emph{e.g.},\xspace Twitter16~\cite{ma_detect_2017}, Weibo~\cite{ma2016detecting}, Pheme~\cite{kochkina2017turing} are intended for detecting rumours, satires, misinformation, etc. (2) They provide accurate ground truth labels, which are collected using fact-checking websites. As a result, they are more accurate than those datasets where news items are weakly labelled by applying distant supervision techniques. For example, the datasets of ReCOVery~\cite{zhou2020recovery} and CoAID~\cite{cui2020coaid} label news records based on the reliability of the source. (3) They provide social context data, which is missing in datasets such as BuzzFeedNews~\cite{santia2018buzzface}, Ma-Twitter~\cite{ma2016detecting} and LIAR~\cite{wang_liar_2017}. This type of information is required by the chosen propagation-based approach to construct the propagation network for each news item.

\begin{table}[t!]
    \centering
    \caption{Sstatistics of the PolitiFact and GossipCop datasets.}
    \begin{tabular}{|c|c|c|}
    \hline
         Dataset & No. of Fake News & No. of Real News \\
         \hline
         PolitiFact & 185 & 225 \\
         \hline
         GossipCop & 4942 & 2520 \\
         \hline
    \end{tabular}
    \label{tab:dataset}
\end{table}

The statistics of the dataset are listed in Table~\ref{tab:dataset}. Note that the values are smaller than those reported in~\cite{shu_fakenewsnet_2018} because (1) many news items, especially fake news, have already been removed, (2) some tweets and retweets of a news items are no longer retrievable, and we cannot build the corresponding propagation pattern. Therefore, those news items are also excluded from our experiments.

\begin{table}[t!]
\caption{Performance comparison of fake news detection on the dataset of PolitiFact}
    \begin{threeparttable}
    \centering
    \begin{tabular}{c|c|c|c|c|c|c}
        \hline
        \ K\tnote{1} \ & \ T\tnote{1} \ & P\tnote{1} \ & Accuracy & Precision & Recall & F1\\
        \hline
        \checkmark & & & 0.837 & 0.844 & 0.801 & 0.836\\
        \hline
        & \checkmark & & 0.829	& 0.838	& 0.824	& 0.826\\
        \hline
        & & \checkmark & 0.854	& 0.855	& 0.852	& 0.852\\
        \hline
        \checkmark & \checkmark & & 0.858	& 0.853	& 0.842	& 0.857\\
        \hline
        & \checkmark & \checkmark & 0.884	& \textbf{0.922}	& 0.825	& 0.883\\
        \hline
        \checkmark & & \checkmark & 0.876	& 0.862	& 0.877	& 0.876\\
        \hline
        \checkmark & \checkmark & \checkmark & \textbf{0.919} & \textbf{0.913} & \textbf{0.913} & \textbf{0.919}\\
        \hline
        \hline
        \multicolumn{3}{c|}{Avg bias~\cite{pan_content_2018}} & 0.752 & 0.725 & 0.776 & 0.729\\
        \hline
        \multicolumn{3}{c|}{Max bias~\cite{pan_content_2018}} & 0.662 & 0.617 & 0.638 & 0.618\\
        \hline
    \end{tabular}
    \begin{tablenotes}
     \item[1] K: knowledge-based, T: text-based~\cite{yang_hierarchical_2016}, P: propagation-based~\cite{han_graph_2020}
   \end{tablenotes}
    \end{threeparttable}
    \label{tab:exp_plt}
\end{table}

\begin{table}[t!]
\caption{Performance comparison of fake news detection on the dataset of GossipCop}
    \begin{threeparttable}
    \centering
    \begin{tabular}{c|c|c|c|c|c|c}
        \hline
        \ K\tnote{1} \ & \ T\tnote{1} \ & P\tnote{1} \ & Accuracy & Precision & Recall & F1\\
        \hline
        \checkmark & & & 0.783 & 0.813 & 0.870 & 0.750\\
        \hline
        & \checkmark & & 0.788 & 0.767 & 0.747 & 0.754\\
        \hline
        & & \checkmark & \textbf{0.872} & 0.855 & 0.860 & \textbf{0.858}\\
        \hline
        \checkmark & \checkmark & & 0.800 & 0.823 & 0.888 & 0.768\\
        \hline
        & \checkmark & \checkmark & 0.861 & 0.882 & \textbf{0.910} & 0.843\\
        \hline
        \checkmark & & \checkmark & \textbf{0.879} & \textbf{0.916} & 0.899 & \textbf{0.866}\\
        \hline
        \checkmark & \checkmark & \checkmark & \textbf{0.871} & 0.903 & \textbf{0.900} & \textbf{0.856}\\
        \hline
        \hline
        \multicolumn{3}{c|}{Avg bias~\cite{pan_content_2018}} & 0.749 & 0.646 & 0.621 & 0.629\\
        \hline
        \multicolumn{3}{c|}{Max bias~\cite{pan_content_2018}} & 0.723 & 0.625 & 0.627 & 0.626\\
        \hline
    \end{tabular}
    \begin{tablenotes}
    \item[1] K: knowledge-based, T: text-based~\cite{yang_hierarchical_2016}, P: propagation-based~\cite{han_graph_2020}
    \end{tablenotes}
    \end{threeparttable}
    \label{tab:exp_gsp}
\end{table}

\subsection{Baselines}
Three baselines are considered: in addition to the two models chosen in Section~\ref{sec:multimodal}, \emph{i.e.,}\xspace test-based~\cite{yang_hierarchical_2016} and propagation-based~\cite{han_graph_2020}, we also implement the knowledge-based method~\cite{pan_content_2018} described in the introduction.

In order to compare these models, the datasets are split as follows: 70\% of the data are used for training, 15\% are for validation, and the remaining 15\% are for test. In addition, all models are evaluated with the following commonly used metrics: accuracy, precision, recall and F1 score.

\myparagraph{Model hyper-parameters.} (1) For the text-based model, both the word embedding dimension and the GRU dimension are set to 100, the learning rate is 0.003, and the batch size is 32 for PolitiFact and 128 for GossipCop. (2) For the propagation-based model, the hyper-parameters for the DiffPool algorithm are: 2 pooling layers, 64 hidden dimensions and 64 embedding dimensions. In addition, the learning rate is 0.001, and the batch size is 128. (3) For our knowledge-based model, the hyper-parameters for SubGNN are selected from the following ranges: batch size \(\in [64, 128]\), learning rate \(\in [3e{\text -}5, \allowbreak 1e{\text -}3]\), number of layers \(\in [1, 4]\), number of structure anchor patches \(|A_{S}| \in [15, 45]\), and feed forward hidden dimension sizes \(\in [32, 64]\) with dropout \(\in [0.0, 0.4]\). (4) For our multi-modal approach, number of feed forward layers \(\in [2, 4]\) with hidden dimension sizes \(\in [8, 64]\) and dropout \(\in [0.0, 0.2]\). (5) For the baseline of~\cite{pan_content_2018}, the embedding dimensions of \(KG_{F}\) and \(KG_{T}\) are in the range of \([30, 50]\) for PolitiFact, and \([50, 100]\) for GossipCop.

\subsection{Performance Comparison of Fake News Detection}
Tables~\ref{tab:exp_plt} and \ref{tab:exp_gsp} show the performance comparison on the two datasets of PolitiFact and GossipCop (results in bold correspond to the best values or values less than \(0.01\) below the best values). The results suggest that:

\begin{itemize}
    \item Our knowledge-based model outperforms the baseline of~\cite{pan_content_2018} on both datasets.
    \item In terms of each single model of \(K,\ T\ \text{and}\ P\), the difference among their performances on the dataset of PolitiFact is not significant, but the propagation-based model achieves the best performance on the dataset of GossipCop, while the other two perform similarly again.
    \item In terms of the multi-modal approach, the performance of any combination of two models is almost always better than each individual model.
    \item If we compare the different combinations, \(K + T < T + P \approx K + P \leq K + T + P\): (1) since the knowledge- and text-based models rely on the same source of information---the news content, the combination of these two performs the worst. (2) When the knowledge- or text-based model is combined with the propagation-based model, they perform similarly and both better than (1). (3) The combination of all three models performs the best overall.
\end{itemize}

The above results suggest that while our knowledge-based approach that does not require any external knowledge graph is effective, combining it with other sources of information can further boost its performance.

\subsection{Time sensitivity analysis}
In addition to the metrics of accuracy, precision, recall and F1 score, it is important to understand how the performance of a model changes over time, since in real cases a deployed fake news detection system is likely to face highly dynamic and volatile data.

\begin{figure*}[t!]
\centering
\begin{subfigure}{\columnwidth}
  \centering
  \includegraphics[width=.95\columnwidth]{acc_time_sensitivity_1.pdf}
  \caption{Accuracy}
  \label{figure_acc}
\end{subfigure}
\begin{subfigure}{\columnwidth}
  \centering
  \includegraphics[width=.95\columnwidth]{pre_time_sensitivity_1.pdf}
  \caption{Precision}
  \label{figure_pre}
\end{subfigure}
\begin{subfigure}{\columnwidth}
  \centering
  \includegraphics[width=.95\columnwidth]{rec_time_sensitivity_1.pdf}
  \caption{Recall}
  \label{figure_rec}
\end{subfigure}
\begin{subfigure}{\columnwidth}
  \centering
  \includegraphics[width=.95\columnwidth]{f1_time_sensitivity_1.pdf}
  \caption{F1}
  \label{figure_f1}
\end{subfigure}
\caption{Time sensitivity analysis for the dataset of GossipCop. The x-axis is the number of weeks from the test item to the last training item.}
\label{figure_time_sensitivity}
\end{figure*}

We run time sensitivity analysis over a period of 26 weeks (half a year) on the dataset of GossipCop  (the dataset of PolitiFact contains only around 400 news items spread over a number of months, which are insufficient to run the analysis): all news items are sorted by their timestamp, and the first 70\% are used for training while the last 30\% are for testing. During test time the data are divided into separate groups based on the number of weeks from the test news item to the last training item, then the four metrics are calculated over each group, \emph{i.e.,}\xspace the model performance is measured weekly. Fig.~\ref{figure_time_sensitivity} shows how different models perform over time.

We can see that (1) the multi-modal approach performs the best in most cases. (2) In the first few weeks, the performance drop of the text-based and the knowledge-based approaches is more obvious than that of the multi-modal and the propagation-based approaches. (3) All four models are relatively stable between Week 5 and Week 25. The results further confirm the effectiveness of the multi-modal approach that combines extracted knowledge, text content and propagation network.

\section{Related Work}\label{sec:related}
In this section, we provide a brief review of the related work on fake news detection, which has become a popular research problem over recent years. Specifically, following a similar taxonomy in ~\cite{shu_fake_2017,pierri_false_2019}, we classify existing work into three categories: content-based approaches, context-based approaches and mixed approaches, which mainly rely on news content, social context, and a mix of both for detection, respectively. In addition, we also summarise prior work on fake news early detection and explainability.

\subsection{Content-based Approaches}
The most straightforward content-based approach is to consider fake news detection as a text classification problem, and apply techniques such as RST~\cite{rubin_towards_2015}, LIWC~\cite{pennebaker_development_2015} and text-CNN~\cite{kim_convolutional_2014} to identify fake news. These algorithms often serve as baselines.

In addition to the knowledge-based detection methods discussed in this paper, another line of research studies fake news from a style-based perspective.

\subsubsection{Style-based Detection}
Since the purpose of fake news is to mislead the public, they should exhibit unique writing styles that are rarely seen in real news. This is supported by forensic psychological studies~\cite{undeutsch_beurteilung_1967}, which have shown that statements based on factual experiences differ from those derived from fabrication or fiction in both content and quality.

Therefore, style-based methods aim to identify the different content style, which can be represented by quantifiable features, including attribute-based language features or structure-based language features. For example, Perez-Rosas \emph{et~al.}\xspace~\cite{perez-rosas_automatic_2018} train linear SVMs on the following linguistic features to detect fake news: unigrams, bigrams, punctuation, psycholinguistic, readability and syntax features. Other methods that fall into this category include~\cite{horne_this_2017,volkova_separating_2017,wang_liar_2017,potthast_stylometric_2018}.

In addition to textual information, images posted in social media have also been investigated to facilitate the detection of fake news~\cite{jin_novel_2017,yang_ti-cnn_2018,wang_eann_2018,zhou_safe_2020}.

\subsection{Context-based Approaches}
Social context here refers to the interactions between users, including tweet, retweet, reply, mention and follow. As mentioned in the introduction, these engagements can form the propagation pattern for a news item, and a number of studies have used various types of models to identify the difference in the propagation pattern between real and fake news: Wu \emph{et~al.}\xspace~\cite{wu_false_2015} use a hybrid SVM; Ma \emph{et~al.}\xspace~\cite{ma_detect_2017} use Propagation Tree  Kernel; Wu \emph{et~al.}\xspace~\cite{wu_tracing_2018} incorporate LSTM cells into the RNN model; Liu \emph{et~al.}\xspace~\cite{liu_early_2018} use both RNNs and CNNs; Shu \emph{et~al.}\xspace~\cite{shu_hierarchical_2019} and Zhou \emph{et~al.}\xspace~\cite{zhou_network-based_2019} propose different types of features and compare multiple commonly used machine learning models; Monti \emph{et~al.}\xspace~\cite{monti_fake_2019}, Lu \emph{et~al.}\xspace~\cite{lu_gcan_2020} and Bian \emph{et~al.}\xspace~\cite{bian_rumor_2020} apply GNNs to study propagation patterns.

In addition to the propagation network, other types of graphs can also be built from social context. For example, Jin \emph{et~al.}\xspace~\cite{jin_news_2016} build a stance network where the weight of an edge represents how much each pair of posts support or deny each other. Then the credibility of all the posts related to a news item is estimated to decide whether the news is fake or real, which can be formalised as a graph optimisation problem.

In another example, Tacchini \emph{et~al.}\xspace~\cite{tacchini_like_2017} propose to detect fake news based on users who liked them on Facebook. They tested logistic regression-based and harmonic Boolean label crowdsourcing-based methods, and their results suggest that both methods can achieve high accuracy.

While all the above methods are supervised, an unsupervised approach is proposed by Yang \emph{et~al.}\xspace~\cite{yang_unsupervised_2019}, which builds a Bayesian probability graphical model to capture the generative process among the validity of news, user opinions and user credibility.

\subsection{Mixed Approaches}
Since both news content and social context can provide valuable evidence, mixed approaches use these two sources of information to differentiate between fake news and real news.

Ruchansky \emph{et~al.}\xspace~\cite{ruchansky_csi_2017} design a three-module architecture that combines the text of a news article, the received user response and the source of the news: (1) in order to capture temporal representations of articles, the first module trains a RNN that takes the user response, news content and user features as input; (2) the second module generates for each user a score and a low-dimensional representation based on user features; (3) the third module takes the output of the first two modules and trains a neural network to label the news item.

A pre-extracted word set is used in~\cite{zhang_fakedetector_2018} to construct explicit features from the news content, user profile and news subject description. Meanwhile, RNNs are applied to learn latent features, such as news article content information inconsistency and profile latent patterns. Once the features are obtained, a deep diffusive network is built to learn the representations of news articles, creators and subjects.

Shu \emph{et~al.}\xspace~\cite{shu_beyond_2019} propose to use the tri-relationship among publishers, news articles and users to detect fake news. Specifically, the latent representations for news content and users are learned with non-negative matrix factorization, and the problem is formalised as an optimisation over the linear combination of each relation. They compare a number of machine learning algorithms to solve the optimisation problem in their experiments.

\subsection{Explainability}
In addition to the above work, a few recent papers have started to work on explainability, which provides evidence to support why their model labels certain news items as fake/real~\cite{popat_declare_2018,shu_defend_2019,lu_gcan_2020}.

For example, Lu \emph{et~al.}\xspace~\cite{lu_gcan_2020} propose a novel attention mechanism for fake news detection which jointly considers news textual content, retweet sequences in propagation networks, and user co-occurrence networks.

\subsection{Fake News Early Detection}
Considering that it is difficult to correct people's perception towards an issue, even if the previous impression is inaccurate~\cite{keersmaecker_fake_2017}, it is more crucial to detect fake news at an early stage before it becomes widespread. Therefore, another line of research works on early detection of fake news~\cite{liu_early_2018,shu_leveraging_2020,zhou_fake_2020}. Shu \emph{et~al.}\xspace~\cite{shu_leveraging_2020} study multiple weak signals of user sentiment, bias and credibility, and then combine weakly labelled data with a small amount of manually labelled data to train a fake news detection model.

\section{Conclusions and Future Work}\label{sec:conc}
A series of incidents over recent years have demonstrated the significant damage that fake news can cause to society. In this work, we investigate knowledge-enhanced multi-modal techniques for fake news detection. Specifically, we transform the problem of detecting fake news into a subgraph classification task, and design a knowledge-based algorithm that does not require any external knowledge graph. In addition, we propose a multi-modal detection algorithm that combines extracted knowledge, textual content and social context. Experimental results on two datasets with thousands of labelled news items demonstrate the effectiveness of our approaches.

For future work, we will further explore more sophisticated methods rather than simple concatenation to combine different sources of information. Moreover, in addition to news content and propagation networks, we intend to exploit other modalities as well, including images and external knowledge graphs.

\bibliographystyle{ACM-Reference-Format}

redpajama_set_name: RedPajamaArXiv


text:
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I finally got around to watching Inception last night. I wanted to see it long before the Inception hype began, around the time I read that a film directed by Christopher Nolan, and starring Ellen Page, Marion Cotillard and DiCaprio was going to come out; and especially after the word "dreams" was thrown in (I, kinda, have a soft spot for films about dreams and the concept of dreams in general).
I'm a big fan of Nolan's work, especially since he did Batman Begins in 2005, (another soft spot here, Batman) and then The Dark Knight and now this.
Again, I'm not going to analyze or critique Inception, but I will express my absolute awe by saying that it was an impeccably put together movie, flawless in every possible way and with an amazing array of actors. (oh, that did come across as a critique...) I am still shocked by how amazing all the imagery was. The scene where Ariadne, (even the choice of the name and the connection with the mythical Ariadne are awesome) realizes she's in a dream, with all the explosions in the street, and the scene where she "bends" the city rendered me plainly speechless!
Also the video below, which gave me the chills, comes to prove how meticulously they put everything together. Here's to hoping that we'll see a featurette about the research they did on dreams for this film.
Note: The above video was not created by me, I found it via an article on the Guardian.


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Home Tags International Space Station
Tag: International Space Station
Release of New E-Book Focused on Service Robots announced, showcasing how...
ELE Times - July 24, 2018
Mouser Electronics, Inc. announces the release of a new e-book titled Service Robots, the most recent offering in the Generation Robot series, part of...
Watch how Mouser Electronics' ISS Design Challenge Winning Device is 3D-Printed...
ELE Times - May 5, 2017
Mouser Electronics Inc. and Grant Imahara on May 3, 3017, released official footage of the winning design of the International Space Station (I.S.S.) Design...
ISRO plans for spacecraft docking and berthing to transfer humans on...
ISRO's plan to develop technology that will allow two space vehicles to attach in orbit and also transfer material between them — described in...
Mouser proudly announces its winning design is 3D-Printed in Space
ELE Times - April 18, 2017
Mouser Electronics and Grant Imahara today announced that the winning design in the International Space Station (I.S.S.) Design Challenge has been 3D-printed in space....
SpaceX creates history: successfully launched and landed used rocket
ELE Times - March 31, 2017
After more than two years of landing its rockets after launch, SpaceX finally sent one of its used Falcon 9s back into space. The...


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West Pakistan Refugees, Gorkhas, get domicile certificate as Div Com organises special camp
File Photo of West Pakistan Refugees.
Srinagar: The process of distribution of Domicile Certificates under the new Domicile law was kick started here today with the distribution of more than 50 Domicile Certificates by Divisional Commissioner, Jammu Sanjeev Verma in a special camp organised for the purpose.
The recipients of the Domicile Certificates included those sections of the society who were residing in J&K for the last more than 70 years but were deprived of their legitimate citizenship rights.
They included West Pakistani refugees, people belonging to the downtrodden sections such as the Balmiki Samaj, Gorkhas and others from R S Pura, Bishnah, Bahu, Jammu South and several other parts of Jammu. Many senior citizens also participated in the camp.
Later, the Divisional Commissioner, while speaking on the occasion, said that with the distribution of Domicile certificates the long pending demand of the public have been fulfilled. People who were living in Jammu and Kashmir for last many years but were deprived of citizenship rights now have equal rights as other citizens of Jammu and Kashmir.
Mayor JMC, Chander Mohan Gupta, Deputy Mayor JMC, Purnima Sharma, Additional Commissioner Jammu, Rakesh Sarangal, Assistant Commissioner Central, Kulbhushan Khajuria, Assistant Commissioner Revenue Jammu, Vijay Sharma and other concerned officer were present on the occasion.
Under the Domicile Rules all those persons and their children who have resided for 15 years in Jammu and Kashmir or have studied for seven years and appeared in class 10th or 12th examination in an educational institution in the UT are eligible for grant of domicile.
Children of Central Government officials, All India service Officers, officials of PSUs and Autonomous body of Central Government, Public Sector Banks, Officials of Statutory bodies, Officials of Central Universities and recognized research institutes of the Central Government, who have served in the Union territory of Jammu of Kashmir for a total period of ten years will also be eligible for Domicile status in the UT.
Besides, all those migrants and their children who are registered with Relief and Rehabilitation Commissioner will be granted Domicile certificate.
Children of those residents of Jammu and Kashmir who reside outside the Union territory in connection with their employment of business or other professional or vocational reasons have become eligible for grant of domicile status.
West Pakistan Refugees (WPRs), Safai Karamcharis and Children of Women married outside Jammu and Kashmir who were earlier deprived are also eligible for Domicile Certificate.
West Pakistani Refugees were part of the Parliamentary electoral roll but not of the erstwhile state electoral roll. They will now be covered under the 15 year residence rule or their children under the 7 year/ class 10/12 rule.
Related Items:Demographic change, Domicile, Governance, Jammu, Kashmir, News
After Chinese fighter aircraft activity, India deploys advanced air defence system in Ladakh
China grabbed 45,000 sq km of Indian land post-1962 war: NCP chief Sharad Pawar


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Bermuda egy szigetcsoport az Atlanti-óceánban. Jogilag Nagy-Britannia tengerentúli területe. 150 szigetből és mintegy 200 korallzátonyból áll. A szigetek közül csupán 20 lakott, ezek közül tízet hidak kötnek össze.

Földrajz

A Bermuda-sziget éghajlata szubtrópusi. Szeptemberben általában több hurrikán is végigsöpör rajta.

Legmagasabb pontja a Town Hill (79 m).

Több nemzeti park is található a szigeten.

Történelem
Bermudát az európaiak az 1500-as évek elején fedezték fel, néhány forrás alapján valószínűleg 1503-ban. Egészen biztosan ismert volt 1511-ben, amikor Peter Martyr d'Angheira megjelentette Legatio Babylonica című művét. Abban az évben egy spanyol térkép is ábrázolja a szigetet. A sziget felfedezését Juan de Bermúdez spanyol felfedező nevéhez szokás kötni. A szigeteket spanyol és portugál hajók használták élelmiszer és ivóvíz készletük kiegészítésére, de a szellemekről és ördögökről szóló legendák, amit táplált egy rémületes madárhang (legvalószínűbben a bermudai viharmadáré), és a folyamatosan viharos, hajótöréssel fenyegető időjárás (a legtöbb korai látogató ezt tapasztalta), elvette a kedvet állandó település alapításától az Ördögszigeten.

Bermúdez de Oviedo és Gonzalo Fernández de Oviedo 1514-ben vagy 1515-ben hordólerakatot kívánt alapítani a szigeten az áthaladó hajók élelmiszerrel és friss vízzel való ellátására. A rossz időjárás azonban megakadályozta a partraszállást. Néhány évvel később egy Santo Domingóból jövő portugál hajó beékelődött a korallzátony sziklái közé. Legénysége életét mentve a következő négy hónapban új hajótestet épített bermudai cédrusból (Juniperus bermudiana L.) és visszatért eredeti kiindulási helyére. Legvalószínűbb, hogy ők hagyták egy sziklába az R és P betűket és az 1543 évszámot. A betűk valószínűleg a "Rex Portugaliae" kifejezés rövidítése és később pontatlanul kapcsolták őket a spanyolokhoz, amikor elnevezték a Bermuda peremén levő sziklákat.

A következő évszázadokban a szigeteket gyakran látogatták, de nem volt állandó település ott. Amikor Virginiában az első két település tönkrement, Bermudát I. Jakab angol és skót király a Virginia Társaságnak (The Virginia Company) adományozta. 1609-ben egy flottilla futott ki Angliából a Társaság tengernagya, Sir George Somers parancsnoksága alatt, hogy újjáélessze a két évvel korábban alapított Jamestownt Virginiában. A flottillát szétszórta a vihar, a Sea Venture zászlóshajó zátonyra futott Bermudánál, (az esemény belekerült Bermuda címerébe), a túlélők vezetői lettek az új terület birtokosai. (William Shakespeare A vihar című színművét William Strachey beszámolója ihlette.) A szigetet a brit korona nevében birtokba vették, és a Virginia Társaság alapító okmányát kiterjesztették rá. 1615-ben a szigetet egy új társaság, a Somers Isles Company (Somers Isles maradt a gyarmat hivatalos neve sokáig) tulajdonába adták át, amelyet ugyanazok a részvényesek alapítottak, mint a Virginia Társaságot. Bermuda szoros virginiai kapcsolatára emlékeztetnek olyan virginiai helynevek, mint Bermuda City vagy Bermuda Hundred. Bermudán verték az első amerikai brit pénzérméket.

A Sea Venture legtöbb túlélője 1610-ben érkezett Jamestown-ba két bermudai építésű hajón. Köztük volt John Rolfe, aki feleségét és gyermekét Bermudán temette el, de Jamestownban feleségül vette Pocahontast, Powhatan leányát. Rolfe egy személyben tartotta kezében Virginia kezdeti dohányiparát (a gyarmat fő gazdasági tevékenysége a fakitermelés megkezdéséig).

Bermudán a szándékos letelepedés a Plough nevű hajó 1612-es érkezésével kezdődött.

A korlátozott földterület miatt Bermudán mindig nehéz volt a lakosság növelése. A letelepülés első két évszázadában a kiegyensúlyozott bevándorlás miatt a népesség kezelhető nagyságú maradt. Az amerikai forradalom előtt több mint tízezer bermudai emigrált, főleg az amerikai Délre, ahol Nagy-Britannia átvette Spanyolországtól a domináns európai hatalom szerepét. A folyamatos elvándorlás folytatódott a 19. század végéig. Akkoriban a tengerjárás volt az egyetlen tömegeket foglalkoztató gazdasági ág, volt idő, amikor a sziget férfilakosságának egyharmada volt egy időben a tengeren. A föld és erőforrások korlátozottsága vezetett az Újvilág első természetvédelmi törvényéig, 1616-ban és 1620-ban törvénnyel tiltották be bizonyos madarak és fiatal teknősök vadászatát.

1649-ben az angol polgárháború jelentős eseménye volt I. Károly angol király kivégzése Londonban. Ennek hírére kitört a bermudai polgárháború; milícia alapítása lett a vége. Ezt a koronához hű bermudai többség hozta létre és elnyomta a puritánokat és independenseket. Őket azért küldték száműzetésbe Bahamára, mert megtagadták a hűségesküt a királyra.

A 17. században a Somers Isles Company leépítette a hajógyártást, a szigetlakókat földművelésre szorította, mert az nagyobb jövedelmet hozott. De a mezőgazdálkodás csak korlátozott eredményeket hozott. Bermudai cédrusból készült dobozokat használtak a dohány Angliába szállítására. Ezek a dobozok többet értek, mint a bennük levő dohány. A virginiai dohány felülmúlta a bermudait mind mennyiségben, mind minőségben. A Somers Isle Company feloszlatása után a szigetlakók azonnal abbahagyták a mezőgazdálkodást, visszatértek a hajóépítéshez, földjeiken a gyorsan növő bermudai cédrust (Juniperus bermudiana) termesztették. Miután megszerezték a gyakorlati ellenőrzést a Turks-szigetek felett, a bermudaiak kiirtották az ottani erdőket, és sókereskedelembe kezdtek, ami Bermuda gazdaságának sarokköve maradt a következő évszázadban.

A bermudai tengerészek nem csak sókereskedelemmel foglalkoztak. Jelentős volt a bálnavadászat, a brit korona nevében folyó kalózkodás és a kereskedelmi hajózás. A bermudai szlup sebességéről és manőverező képességéről volt ismert. A trafalgari csata után a Pickle nevű bermudai szlup, ami akkor a Brit Királyi Haditengerészet egyik leggyorsabb hajója volt, vitte meg Angliába a győzelem és Nelson halálának hírét.

Az amerikai függetlenségi háború után a Királyi Haditengerészet kikötőt és hajógyárat épített Ireland-szigeten, a szigetlánc nyugati végén, a nyugati Atlanti-óceán hajóútjainak ellenőrzésére. Az 1815-ös angol-amerikai háború idején itt volt az angol flotta egyik fontos támaszpontja. Innen indították azokat a támadásokat, amelyek során többek között Washingtont is elfoglalták rövid időre. Később a bermudai Királyi Hajógyárat ismét megerősítették az amerikai támadás veszélye miatt. A munkát James Arnold vezette 1816-ban. Arnold a hírhedt amerikai áruló, Benedict Arnold fia volt. Manapság a Maritime Museum (Tengeri Múzeum) működik a Királyi Hadihajógyár egykori főbiztosi házában. A múzeum a támaszpont katonai történetét mutatja be.

Mivel Bermuda aránylag közel fekszik az Egyesült Államok délkeleti partjaihoz, az amerikai polgárháború idején a déliek blokádkerülő hajói rendszeresen a szigetekről indultak, hogy az északiak hadihajóit elkerülve brit hadianyagot szállítsanak a délieknek. St. George's-ban a régi Globe Hotel volt a déli ügynökök törzshelye. Ez ma múzeum.

A korai huszadik században, a modern szállítási és hírközlési rendszer kialakulásával Bermuda a gazdag amerikai, kanadai és brit turisták népszerű úti célja lett. Az Egyesült Államok 1930-as vámtarifa-törvénye nyomán Bermudán jelentős turistaipar fejlődött ki. 1994-ben Henry Vassey, a Bermudai Kereskedelemfejlesztési Hatóság vezetője indítványozta a bermudai képviselőháznak, hogy csatlakozzanak Kanadához. Bermuda négy metodista gyülekezete a Kanadai Egyesült Egyházhoz csatlakozott, létrehozva az Egyesült Egyház Tengermelléki Konferenciájának (székhelye Sackwille, Új-Brunswick) Bermudai Presbitériumát.

Népesség

A népesség közel, 55-60%-a fekete bőrű, részben az afrikai rabszolgák, részben pedig a nyugat-indiai szigetvilág akkorra már felszabadult, és Bermudára emigrált néger lakosainak leszármazottai. Az európai lakosság aránya: 34-36%, kizárólag a brit gyarmatosítók utódai, szép számmal akadnak köztük szegény ír és skót telepesek leszármazottai. 6% mulatt, azaz fekete-fehér keverék. További 4% indiai bevándorló, akik akkor érkeztek a szigetre, amikor még India is a Brit Birodalomhoz tartozott.

Politika
A szigetcsoport a legrégebbi önkormányzattal rendelkező kolóniája az Egyesült Királyságnak, 1620-ban alapított parlamentje a világon a harmadik legrégebb óta folyamatosan fönnálló törvényhozó testület. Bermuda politikája hagyományosan kétpárti jellegű, a választásokon a főleg feketék által támogatott Progresszív Munkáspárt (Bermuda) (PLP) és a fehérek körében népszerűbb Egy Bermuda Szövetség (OBA) verseng egymással. A Bermudai függetlenségi mozgalom relatíve alacsony támogatottságú a népesség körében, a politikai pártok közül a jelenleg is kormányzó PLP támogatja.

Gazdaság
Fő kiviteli cikkei az illóolajok, gyógyászati alapanyagok, zöldségfélék, vágott virágok.Jelentős bevétel származik a pénzügyi szolgáltatásokból. Szinte minden élelmiszert és közszükségleti cikket importálnak, elsősorban az USA a fő kereskedelmi partner. Az ipara nem jelentős, a sziget mindössze 6%-a művelhető terület. Bermuda saját pénznemmel, a bermudai dollárral rendelkezik.

Kultúra

Közlekedés
 Közutak hossza: 450 km
 Repülőterek száma: 1
 Kikötők száma: 3

Sport

Olimpia

Bermuda eddig két érmet szerzett a játékok során. Clarence Hill bronzérmes lett ökölvívásban 1976-ban, az ország történetének első olimpiai aranyérmét triatlonban Flora Duffy nyerte az egy évvel elhalasztott 2020-as nyári olimpián 2021. július 27-én. Bermuda az első százezer alatti lakosú ország, amelynek olimpiai aranyérme van.

Krikett
Bermudán népszerű sport a krikett. Az első (feljegyzett) mérkőzést már 1844-ben lejátszották, a bermudai válogatott pedig 2006-ban egyike lett a világ azon kevés válogatottjának, amely jogosult volt egynapos nemzetközi mérkőzéseket játszani. 2009-ben azonban gyenge eredményeik miatt ezt a lehetőséget elvesztették.

Labdarúgás

Eredmények
 Pánamerikai játékok
 Ezüstérmes: (1967)
 Közép-amerikai és karibi játékok
 Bronzérmes: (1974, 1978)

Jegyzetek

Források
 Midi világatlasz (Nyír Karta & Topográf Térképészeti Kft., Nyíregyháza, 2004)

További információk

 Bermuda hivatalos honlapja
 Bermuda.lap.hu – Linkgyűjtemény

Kapcsolódó szócikkek
 A Brit-szigetek terminológiája

Brit tengerentúli területek

redpajama_set_name: RedPajamaWikipedia


THE son of a popular Beeston woman paid tribute to his 'kind and beautiful' mother at her inquest.
Dorothy Ennion, 84, a former school secretary of Bunbury Aldersey Primary School and a longstanding member of the Bunbury Women's Institute, died last November of factors arising from a leg injury she sustained after falling over.
An inquest into her death, held at Chester Magistrates Court, established that Mrs Ennion had been suffering from various ailments including deep vein thrombosis and a pulmonary embolism, as well as increasing mobility problems.
But a fall she sustained in the kitchen of her home in Beeston where she lived, caused a haemorrhage which made her condition deteriorate and she died at the Countess of Chester Hospital on November 14 last year.
Mrs Ennion, whose late husband John worked for Cheshire County Council for more than 30 years, was a former pupil of The Girls School, Chester, and regularly attended St Boniface Church in Bunbury.
She was a well-known face in the community thanks to her involvement with the WI and her 25 years working at the school.
After leaving school as a young girl, Dorothy went to work in her uncle's grocery shop in Duddon where she met her future husband John, and they married in 1950.
Their son Robert was born 11 years later, and he became her full-time carer in her later years.
Speaking after her inquest, Mr Ennion said he 'could not have wished for a better mother'.
"My mother was kind and always put others before herself – she was beautiful and her personality and character were faultless.
He added: "Even with all her ailments she continued to enjoy the company of her friends and family and was able to attend the Bunbury WI meetings when she felt up to it.


I enter the bookstore early, prepared and excited about reading a passage from my memoir. Thirty folding chairs are set up around a raised stage complete with microphone and overhead spotlights. The store conducts only one open mic night a month, so I immediately sign my name to be the first one to read to the masses at precisely three o'clock.
Trying to kill the thirty minutes left before my opening oration, I wander through the store, perusing the new memoirs that couldn't be nearly as good as mine – that is, if my memoir were published.
A few minutes before three, I rush to the stage area, but stop when I see all the folding chairs still empty. Worse yet, the sign-up sheet has one name on it – mine. Two chair backs have coats on them, but no one's around.
The host arrives to tell me if I prefer not to use the microphone, it's okay. What's the point of a mic? After all, there's two people here. They could hear me whisper my reading, even from the back row. In fact, I'm too embarrassed to use the stage and stand directly in front of the two women. Since they were kind enough to show up, and have basically saved my day, I'm happy to give them a customized reading.
I read an excerpt from Chapter 13, "Health," of my memoir, Maybe Boomer, about the day my doctor of oriental medicine told me I had Lyme disease. I recite a few pages that act as build-up to that big moment in my life.
Reading along, approaching the part where I talk with my DOM, I glimpse the silhouette of another person entering the store's doors. Maybe it's a third person to hear me read! The silhouette stops, lingers, and listens to my big oratory finish: "Mike, I'm sure now. You have Lyme disease." I pause, then read on. So, just to be sure, I get tested for Lyme. And what do you know? The results come back positive. My DOM was right.
The two women are almost on the edge of their seat in anticipation about what comes next. What else could I ask for (except to not have Lyme disease)? What could possibly top this?
I look beyond them to see the silhouette in full – my DOM.
Coincidence. Smiles. Thrills. Laughs. The two women even ask my DOM for her business card. Happy endings all around.
It's probably the best reading I'll ever have.


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CECC removes Mongolia from its list of medium-risk countries due to the escalating coronavirus situation in Mongolia and moves Singapore and Cambodia to the list of low-risk countries
On November 25, the Central Epidemic Command Center (CECC) announced five new confirmed imported cases of coronavirus disease 2019 (COVID-19) in Taiwan. As of writing, a cumulative total of 108,223 cases related to COVID-19 have been reported in Taiwan among which COVID-19 has been ruled out in 106,548. Of these reported cases, infection with COVID-19 was laboratory-confirmed in 623 cases. Of the 623 confirmed cases, 531 are imported; 55 are indigenous; 36 are naval crew members aboard the Panshi fast combat support ship; 1 case has unknown sources of infection; and 1 case (Case #530) has been removed (Case #530 is not assigned to any patients). Of the confirmed cases, there have been 7 deaths, and 553 patients have been released from isolation, with the other 63 patients remaining hospitalized in isolation.
The CECC also announced the updated list of countries/regions eligible for shortened quarantine periods in Taiwan.
The CECC reported that since the first COVID-19 case was reported in Mongolia on November 11, more than 200 local cases have been reported for the last two weeks. The coronavirus situation in the country continues to worsen and represents the risk of further spread. Therefore, the CECC removes Mongolia from the list of medium-risk countries/regions, effective immediately. Furthermore, as the status of local cases in Singapore and Cambodia has met the criteria for the CECC's list of low-risk countries/regions, Singapore and Cambodia are moved to the list of low-risk countries/regions, effective immediately. The latest list of eligible countries/regions is as follows:
1. Low-risk countries/regions: New Zealand, Macao, Palau, Fiji, Brunei, Thailand, Bhutan, Laos, Nauru, East Timor, Mauritius, Vietnam, the Marshall Islands, Singapore, Cambodia.
2. Medium-risk countries/regions: Hong Kong, Australia.


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Taylor English Decisions Announces Earl Ehrhart As CEO
ATLANTA, April 16, 2018 – Earl Ehrhart, retiring influential Cobb County state representative, today was named as the Chief Executive Officer for Taylor English Decisions LLC, a government strategies and business consulting firm that will provide clients with guidance on improved and innovative business operations. Trey Paris, Jonathan Crumly, and Daraka Satcher have also joined the practice.
Taylor English Decisions is an affiliate of Taylor English Duma LLP.
"The addition of a Government Affairs Practice to Taylor English Decisions will equip our clients with a better understanding of the complex and ever-changing world of government policy and regulation," said Marc Taylor, a founding partner of Taylor English. "We are thrilled that Earl has joined our team to lead this venture alongside our roster of talented professionals, and know that the experience of Trey, Jonathan and Daraka round out our service offerings."
Ehrhart joins the firm as CEO of Taylor English Decisions after serving for 30 years as a member of the Georgia House of Representatives on behalf of Cobb County. During his tenure in government, he chaired the House Rules Committee and oversaw Higher Education Appropriations. Ehrhart also served as the national chair and a 15-year board member for the American Legislative Exchange Council, where he helped provide legislative solutions for complex business and economic issues facing the state.
Previously announced in January, David Connell serves as a principal for the Business Consulting Practice of Taylor English Decisions. Connell previously served as chief executive officer and president of the Cobb Chamber of Commerce. He was involved with the Chamber for 15 years, first as a board member, then as chairman and later as president and CEO. Connell completed a forty-year career at Southern Company before joining the Chamber as its president and CEO.
Trey Paris will lead Taylor English Decision's Government Affairs Practice. He joins from General Electric, where he served as the U.S. manager of state government affairs and policy functions. Prior to GE, Paris was a fourteen-year executive with The Coca-Cola Company. During his tenure he held various management roles in the public affairs arena, overseeing State Government Relations for the U.S., Shareowner Affairs, and Issues Management and Global Communications. He brings more than 30 years of experience in public affairs, government relations, corporate communications, shareholder relations and issue management.
As a principal in Taylor English Decisions, Crumly helps clients comply with government policies and regulations that affect their industries and bottom lines. He is a practicing attorney who offers legislative legal experience necessary to navigate successfully the ever changing political and regulatory environment.
Prior to joining Taylor English Decisions, Satcher was deputy assistant secretary for legislative and intergovernmental affairs at the U.S. Department of Commerce. Additionally, he spent nearly 10 years working in both the U.S. Senate and U.S. House of Representatives.
Taylor English Decisions LLC is a purpose-built business consulting and government relations firm. On the business consulting side, the firm works with senior management to improve operating efficiencies. On the government affairs side, the firm assists its clients in understanding the complex and ever-changing nature of public policy. The firm's goal is to better the relationships between businesses and governments on the federal, state and local level.
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Last Sunday I drove down to Mooresville with three beauties to Anderson Orchard, a place that I've actually been visiting since childhood. Totam also has fond memories of the place. Together we helped guide Cassie and Amanda through rows of trees, picking and tasting apples and excitedly gathering chestnuts from the ground. We shared apples, one bite each, and attempted to describe each type, similar to a sommelier with wine although not quite as elegant.


text:
Private cars drive taxis away from Bhaktapur
An estimated 400 private second-hand cars and vans are being operated in Dudhpati, Bhaktapur Durbar Square, Kamalbinayak, Thimi and Sallaghari.
Private cars and van used to ferry passengers are parked in Dudhpati, Bhaktapur. Anup Ojha/TKP
Anup Ojha
Published at : December 20, 2019
Updated at : December 21, 2019 07:19
Taxi drivers in Bhaktapur have repeatedly complained of the private car owners' syndicate in transporting passengers, which have curtailed their daily earnings. Despite the complaints, the government has turned a blind eye to the drivers' concerns.
Every day the entry gate of Bhaktapur Durbar Square in Dudhpati sees a line of more than three dozen private cars waiting for passengers. Similar is the scene at Bhaktapur Durbar Square, Kamalbinayak, Thimi, Sallaghari, Suryabinayak and Gatthaghar. It is estimated that more than 400 such cars and vans are illegally operating in these areas.
If one has to go to Nagarkot or Dhulikhel, they cannot get a taxi while private Suzuki cars are easily available to rent, Bhaktapur residents say.
"This is a syndicate of locals. They are operating their private cars as taxis. They do not let us park in those areas. Still, the local authority has not shown any concern over our repeated complaints. The authority has permitted them to run those cars illegally," said Jeevan Lama, 47, a taxi driver based in Sallaghari.
The Motor Vehicles and Transportation Management Act-1993 does not allow the operation of private vehicles for purposes other than specified. In the third week of November, the government issued a second notice in a bid to stop popular ride-sharing services. But, after widespread criticism, the government backtracked on its decision.
"They buy an old car and operate it as a taxi, and they have been earning good money illegally. There is no one to monitor them," said Lama, who is the sole breadwinner of a seven-member family. "If we take passengers to Bhaktapur Durbar Square, we have to return empty, because private car owners won't let us carry new passengers."
Taxi drivers complain that they can't stop for passengers in Dudhpati, Suryabinayak and other areas as the local car owners chase them out.
When the Post approached private car operators to inquire why they were running their cars as taxis, many said tourists are their only source of income. "I bought this car with Rs300,000, taking a loan as I don't have any land. And this is feeding my family," said Rajindra Chawala, a residence of Bansa Gopal Chowk, who was parking his car a few meters east of Siddha Pokhari.
Chawala said he has been into this business for more than a decade. "In my case, I am doing this as I don't have an alternative, but there are some rich people too, who are using their cars to ferry passengers and are making good money."
When the Post contacted Inspector Hari Om Khadka, in charge of the Sallaghari Traffic Police Circle, regarding the illegal use of private cars, he said the police have been cracking down on them. "We have nabbed them many times. We slap them with Rs 1,000 fines, but they do it repeatedly. We don't have enough traffic officials to monitor the area," said Khadka.
When the Post contacted Senior Superintendent Bhim Prasad Dhakal, chief of the Metropolitan Traffic Police Division, he said personnel would be deployed immediately to look into the issue of illegal taxi operators.
Anup Ojha is a reporter for The Kathmandu Post primarily covering social issues and human interest stories. Before moving to the social beat, Ojha covered arts and culture for the Post for four years.
Man may have killed wife, son, officials investigating Thimi case say
Bhaktapur opens six tourist centers to engage workers in cleaning and beautification works
Eight years on, Guthi has been barred from leasing land over court case
In this Nepali city, the North Korean dream is alive—and it's thriving
Police arrest two men from Changunarayan for alleged motorcycle theft
Bhaktapur Municipality has installed open gym facilities and people are happy exercising


text:
Q: Assert equal in xunit and exclude a field i have a test where i will be comparing two objects.
i am open to know whats the best way to do it.
i have created something for which i have an issue that needs help.

*

*following code has an object property that needs to be present

*i would like to assert that all fields to be present except the id property.

*i feel like the last 5 statements feel inappropriate, if there is a clearer way of doing it, i would like to know.

    [Fact]
    public void CreateTransaction_AddFirstTransaction_ShouldUpdateTransactionJson()
    {
        // Arrange
        var mockFileSystem = new MockFileSystem();
        var buyCrypto = new BuyCrypto(mockFileSystem);
        var bitcoin = new Currency()
        {
            name = "bitcoin",
            code = "btc",
            price = 10
        };

        // Act
        buyCrypto.CreateTransaction(true, bitcoin, 10);

        //Assert
        var result = JsonSerializer
            .Deserialize<List<Transaction>>(mockFileSystem.GetFile(TransactionJson).TextContents);

        Assert.Equal("bitcoin", result[0].currency);
        Assert.Equal(DateTime.Now.ToString(), result[0].dateTime);
        Assert.Equal("TestName", result[0].name);
        Assert.Equal(10, result[0].quantity);
        Assert.Equal(100, result[0].total);
    }

A: I love using Fluent Assertions for these kinds of tests (docs). You could do something like this:
// Arrange
// ... other stuff
var expectedTransaction = new Transaction {
    currency = "bitcoin",
    dateTime = DateTime.Now.ToString(),
    name = "TestName",
    quantity = 10,
    total = 100 };

// Act
// ...

// Assert
result[0].Should().BeEquivalentTo(expectedTransaction, options => options.Excluding(t => t.Id));

redpajama_set_name: RedPajamaStackExchange


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text:
Der Kinkéliba (Combretum micranthum) ist eine Pflanzenart aus der Gattung der Langfäden (Combretum). In der Mandinka-Sprache wird er Kinkiliba genannt und auf Wolof Segweou.

Vorkommen
Combretum micranthum ist in Westafrika heimisch. Der Kinkéliba ist eine der häufigsten Arten des Tigerbusch und kommt oft auf Inselbergen und Standorten mit Lateritkruste vor.

Beschreibung
Combretum micranthum wächst als laubabwerfender Strauch und erreicht eine Wuchshöhe bis zu 5 Meter oder als Baum bis zu 10 Meter. Manchmal wächst sie auch als Kletterpflanze viele Meter weit oder hoch.

Die einfachen und kurz gestielten Laubblätter sind gegenständig oder wirtelig zu dritt angeordnet. Sie sind eiförmig bis verkehrt-eiförmig oder elliptisch, ganzrandig, rundspitzig bis bespitzt, bewimpert und unterseits schuppig. Die Nebenblätter fehlen.

Es werden kurze und achselständige, schuppige und behaarte Ähren gebildet. Die sehr kleinen, sitzenden, zwittrigen Blüten sind vierzählig mit doppelter Blütenhülle. Der kleine Blütenbecher ist zweiteilig, der untere, etwa 2 Millimeter lange, stielartige Teil ist röhrig, der obere ist becherförmig. Die dreieckigen Kelchblätter sind klein. Die weißen bis cremefarbenen Petalen sind spatelförmig und 1,5 Millimeter lang. Es sind 8 vorstehende Staubblätter vorhanden. Der unterständige Fruchtknoten ist einkammerig mit kurzem Griffel.

Es werden vierflügelige, eiförmige bis rundliche, rot-braune, bis 1,5 Zentimeter große, einsamige und nicht öffnende Flügelfrüchte gebildet. Die breiten Flügel sind bis 7 Millimeter breit.

Die Chromosomenzahl beträgt 2n = 26.

Nutzung
Der Kinkéliba (auch in der Schreibweise Quinquéliba) ist gut an das trockene Klima der Sahelzone angepasst und wird als Futterpflanze für Wiederkäuer verwendet. Während der Regenzeit wird es vor allem von Schafen gefressen. Es kommt vor allem in tiefer liegenden Weidegründen vor.

Der Kinkéliba besitzt zahlreiche Anwendungen in der traditionellen Medizin. So wird ein Tee aus den Blättern vorbeugend gegen Malaria getrunken, die frischen Blätter werden gegen Bauchschmerzen und Durchfall gekaut. Ein Sud aus den Wurzeln soll gegen Syphilis und Wurmbefall helfen.

Literatur
 Jean Berhaut: Dicotylédones. Balanophoracées à Composées (Flore Illustrée du Sénégal; Bd. 2). Gouvernement du Sénégal, Dakar 1974 (Vorwort von Léopold Sédar Senghor).
 Michel Arbonnier: Arbres, arbustes et lianes des zones seches d'Afrique de l'Ouest. CIRAD, Montpellier 2004, ISBN 2-87614-579-0, S. 261.
 Hadja O. Sanon, Chantal Kabore-Zoungrana, Inger Ledin: Behaviour of Goats, Sheep and Cattle and Their Selection of Browse Species on Natural Pasture in a Sahelian Area. In: Small Ruminant Research. The official journal of the International Goat Association, Bd. 67, Heft 1, 2007, S. 64–74, doi:10.1016/j.smallrumres.2005.09.025.
 Adjima Thiombiano, Marco Schmidt, Holger Kreft, Sita Guinko: Influence du gradient climatique sur la distribution des espèces de Combretaceae au Burkina Faso (Afrique de l'Ouest) (PDF; 849 kB), In: Candollea. Journal international de Botanique systématique. Bd. 61 2006, S. 189–203, online auf researchgate.net.

Einzelnachweise

Weblinks

 Combretum micranthum bei PROTA.

 Moctar Sacandé, Mathurin Sanon: Seed leaflet No. 129, 2007, (PDF; 614 kB).

Myrtenartige
Heilpflanze
Rohstoff für teeähnliche Getränke


text:
— японский футболист.

Карьера
На протяжении своей футбольной карьеры выступал за клубы «Иокогама Флюгелс», «Иокогама Ф. Маринос», «Касива Рейсол», «Омия Ардия».

Национальная сборная
С 2001 по 2002 год сыграл за национальную сборную Японии 15 матчей. Также участвовал в Кубке конфедераций 2001 года.

Статистика за сборную

Достижения
 Джей-лиги: 2003, 2004
 Кубок Императора: 1998
 Кубок Джей-лиги: 2001

Примечания

Ссылки
 Japan National Football Team Database

Футболисты Японии
Игроки сборной Японии по футболу
Игроки ФК «Иокогама Ф. Маринос»
Игроки ФК «Иокогама Флюгелс»
Игроки ФК «Касива Рейсол»
Игроки ФК «Омия Ардия»