What language is explored in this paper?
English
Online social media platforms have made the world more connected than ever before, thereby making it easier for everyone to spread their content across a wide variety of audiences. Twitter is one such popular platform where people publish tweets to spread their messages to everyone. Twitter allows users to Retweet other users' tweets in order to broadcast it to their network. The more retweets a particular tweet gets, the faster it spreads. This creates incentives for people to obtain artificial growth in the reach of their tweets by using certain blackmarket services to gain inorganic appraisals for their content. In this paper, we attempt to detect such tweets that have been posted on these blackmarket services in order to gain artificially boosted retweets. We use a multitask learning framework to leverage soft parameter sharing between a classification and a regression based task on separate inputs. This allows us to effectively detect tweets that have been posted to these blackmarket services, achieving an F1-score of 0.89 when classifying tweets as blackmarket or genuine.
Twitter is an important medium for people and companies to promote their products, ideologies, or to reach out and connect with other people in the form of micro-conversations. Twitter provides users with multiple ways of showing their support towards a tweet in the form of Likes, Retweets and Quotes. These content-level appraisals help in spreading the content further and act as a measure of users' agreement on the value of the content. The count of these content-level appraisals therefore determines the influence of a particular tweet and its author. This has led to the creation of certain blackmarket services such as FreeFollowers (https://www.freefollowers.io/), Like4Like (https://like4like.org/), YouLikeHits (https://www.youlikehits.com/), JustRetweet (http://justretweet.com), which allow users to post their tweets in order to gain inorganic appraisals in the form of Likes, Retweets and Quotes BIBREF0 , BIBREF1 . There has been a lot of research on the detection of fraudulent activities on Twitter such as detection of bots BIBREF2 , fake followers BIBREF3 , collusive retweeters BIBREF0 , BIBREF1 , and social spam BIBREF4 . However, the problem of detecting tweets that are posted to these blackmarket services has not been tackled before. The tweets submitted to blackmarket services are not necessarily spam or promotional tweets. As we observe in our data, there is some intersection between spammers and blackmarket users since spammers may also try to gain more appraisals by using these services. However, existing spam tweet detection approaches do not work that well in identifying individual tweets as blackmarket tweets (as shown in Table TABREF29 ). Table TABREF1 shows a sample tweet that was posted on a blackmarket service and another sample tweet that was not. In this paper, we make the first attempt to detect tweets that are posted on blackmarket services. Our aim is to build a system that can flag tweets soon after they are posted, which is why we do not consider temporal features such as the number of retweets or likes that a tweet keeps gaining over time. Instead, we only rely on the features and representations extracted from the content of the tweets. We curate a novel dataset of tweets that have been posted to blackmarket services, and a corresponding set of tweets that haven't. We propose a multitask learning approach to combine properties from the characterization of blackmarket tweets via traditional feature extraction, with a deep learning based feature representation of the tweets. We train a neural network which takes as input both the traditional feature representation as well as the deep learning based representation generated using the Tweet2Vec model BIBREF5 , and utilizes cross-stitch units BIBREF6 to learn an optimal combination of shared and task-specific knowledge via soft parameter sharing. We show that our multitask learning approach outperforms Twitter spam detection approaches, as well as state-of-the-art classifiers by 14.1% (in terms of F1-score), achieving an F1-score of 0.89 on our dataset. In short, the contributions of the paper are threefold: a new dataset, characterization of blackmarket tweets, and a novel multitask learning framework to detect tweets posted on blackmarket services.