It is completely amazing! Fake news and click-baits have totally invaded the cyber space. Let us face it: everybody hates them for three simple reasons. Reason #2 will absolutely amaze you. What these can achieve at the time of election will completely blow your mind! Now, we all agree, this cannot go on, you know, somebody has to stop it. So, we did this research on fake news click-bait detection and trust us, it is totally great research, it really is! Make no mistake. This is the best research ever! Seriously, come have a look, we have it all: neural networks, attention mechanism, sentiment lexicons, author profiling, you name it. Lexical features, semantic features, we absolutely have it all. And we have totally tested it, trust us! We have results, and numbers, really big numbers. The best numbers ever! Oh, and analysis, absolutely top notch analysis. Interested? Come read the shocking truth about fake news and click-bait in the Bulgarian cyber space. You won't believe what we have found!
analytics of on-line statements is an emerging research direction @cite_10 . This includes predicting credibility of information shared in social media @cite_13 , stance classification @cite_34 and contradiction detection in rumours @cite_7 . For example, studied the problem of finding false information about a newsworthy event. They compiled their own dataset, focusing on tweets using a variety of features including user reputation, author writing style, and various time-based features. analysed the interaction of content and social network structure, and studied how Twitter users judge truthfulness. They found that this is hard to do based on content alone, and instead users are influenced by heuristics such as user name.
@cite_13 @cite_34 @cite_10 @cite_7
2251523416 2548105042 1802128609 2555891875
Rumours on social media exhibit complex temporal patterns. This paper develops a model of rumour prevalence using a point process, namely a log-Gaussian Cox process, to infer an underlying continuous temporal probabilistic model of post frequencies. To generalize over different rumours, we present a multi-task learning method parametrized by the text in posts which allows data statistics to be shared between groups of similar rumours. Our experiments demonstrate that our model outperforms several strong baseline methods for rumour frequency prediction evaluated on tweets from the 2014 Ferguson riots. A task of primary importance for social network users is to decide whose updates to subscribe to in order to maximize the relevance, credibility, and quality of the information received. To address this problem, we conducted an experiment designed to measure the extent to which different factors in online social networks affect both explicit and implicit judgments of credibility. The results of the study indicate that both the topical content of information sources and social network structure affect source credibility. Based on these results, we designed a novel method of automatically identifying and ranking social network users according to their relevance and expertise for a given topic. We performed empirical studies to compare a variety of alternative ranking algorithms and a proprietary service provided by a commercial website specifically designed for the same purpose. Our findings show a great potential for automatically identifying and ranking credible users for any given topic. The need to assess the trustworthiness of a piece of information has become a prevalent issue due to web vandalism and the openness of platforms such as wikis. In this paper we introduce the notion of contextual accountability: Holding the author of a given statement accountable by deriving a path from the statement to the author’s credentials. We present our own trust vector model as a combination of the assessment of these credentials covering two aspects; knowledgeability and social acceptance. This model is put to the test using two datasets and the results are presented and discussed. The utilization of social media material in journalistic workflows is increasing, demanding automated methods for the identification of mis- and disinformation. Since textual contradiction across social media posts can be a signal of rumorousness, we seek to model how claims in Twitter posts are being textually contradicted. We identify two different contexts in which contradiction emerges: its broader form can be observed across independently posted tweets and its more specific form in threaded conversations. We define how the two scenarios differ in terms of central elements of argumentation: claims and conversation structure. We design and evaluate models for the two scenarios uniformly as 3-way Recognizing Textual Entailment tasks in order to represent claims and conversation structure implicitly in a generic inference model, while previous studies used explicit or no representation of these properties. To address noisy text, our classifiers use simple similarity features derived from the string and part-of-speech level. Corpus statistics reveal distribution differences for these features in contradictory as opposed to non-contradictory tweet relations, and the classifiers yield state of the art performance.