A large body of research work and efforts have been focused on detecting fake news and building online fact-check systems in order to debunk fake news as soon as possible. Despite the existence of these systems, fake news is still wildly shared by online users. It indicates that these systems may not be fully utilized. After detecting fake news, what is the next step to stop people from sharing it? How can we improve the utilization of these fact-check systems? To fill this gap, in this paper, we (i) collect and analyze online users called guardians, who correct misinformation and fake news in online discussions by referring fact-checking URLs; and (ii) propose a novel fact-checking URL recommendation model to encourage the guardians to engage more in fact-checking activities. We found that the guardians usually took less than one day to reply to claims in online conversations and took another day to spread verified information to hundreds of millions of followers. Our proposed recommendation model outperformed four state-of-the-art models by 11 33 . Our source code and dataset are available at this http URL.
Another direction is to detect or classify stances of users (e.g. supporting or denying) toward rumors @cite_8 @cite_2 and to analyze how users' stances have changed over time @cite_0 @cite_57 @cite_64 . In addition to studying rumors' content, researchers @cite_66 @cite_12 also analyzed who were involved in spreading those rumors. Since fake news can be viewed as misinformation, work about detecting content polluters @cite_56 , social bots @cite_46 and malicious campaigns @cite_50 are also related to our work. The following two works @cite_44 @cite_24 are perhaps the most closely related to our work. In particular, @cite_44 analyzed the social relationship between the fact-checking user and the fact-checked user in online conversations. @cite_42 employed fact-checking URLs in Snopes.com as a way to understand how rumors were spread on Facebook. Our work differs from the prior works @cite_44 @cite_24 since we focus on guardians, their temporal behavior and topical interests, and propose a fact-checking URL recommendation model to personalize relevant fact-checking URLs.
@cite_64 @cite_8 @cite_42 @cite_56 @cite_0 @cite_57 @cite_44 @cite_24 @cite_50 @cite_2 @cite_46 @cite_66 @cite_12
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A rumor is commonly defined as a statement whose true value is unverifiable. Rumors may spread misinformation (false information) or disinformation (deliberately false information) on a network of people. Identifying rumors is crucial in online social media where large amounts of information are easily spread across a large network by sources with unverified authority. In this paper, we address the problem of rumor detection in microblogs and explore the effectiveness of 3 categories of features: content-based, network-based, and microblog-specific memes for correctly identifying rumors. Moreover, we show how these features are also effective in identifying disinformers, users who endorse a rumor and further help it to spread. We perform our experiments on more than 10,000 manually annotated tweets collected from Twitter and show how our retrieval model achieves more than 0.95 in Mean Average Precision (MAP). Finally, we believe that our dataset is the first large-scale dataset on rumor detection. It can open new dimensions in analyzing online misinformation and other aspects of microblog conversations. The problem of identifying rumors is of practical importance especially in online social networks, since information can diffuse more rapidly and widely than the offline counterpart. In this paper, we identify characteristics of rumors by examining the following three aspects of diffusion: temporal, structural, and linguistic. For the temporal characteristics, we propose a new periodic time series model that considers daily and external shock cycles, where the model demonstrates that rumor likely have fluctuations over time. We also identify key structural and linguistic differences in the spread of rumors and non-rumors. Our selected features classify rumors with high precision and recall in the range of 87 to 92 , that is higher than other states of the arts on rumor classification. The rise in popularity of social networking sites such as Twitter and Facebook has been paralleled by the rise of unwanted, disruptive entities on these networks- — including spammers, malware disseminators, and other content polluters. Inspired by sociologists working to ensure the success of commons and criminologists focused on deterring vandalism and preventing crime, we present the first long-term study of social honeypots for tempting, profiling, and filtering content polluters in social media. Concretely, we report on our experiences via a seven-month deployment of 60 honeypots on Twitter that resulted in the harvesting of 36,000 candidate content polluters. As part of our study, we (1) examine the harvested Twitter users, including an analysis of link payloads, user behavior over time, and followers following network dynamics and (2) evaluate a wide range of features to investigate the effectiveness of automatic content polluter identification. This study offers an in-depth analysis of four rumors that spread through Twitter after the 2013 Boston Marathon Bombings. Through qualitative and visual analysis, we describe each rumor's origins, changes over time, and relationships between different types of rumoring behavior. We identify several quantitative measures-including temporal progression, domain diversity, lexical diversity and geolocation features-that constitute a multi-dimensional signature for each rumor, and provide evidence supporting the existence of different rumor types. Ultimately these signatures enhance our understanding of how different kinds of rumors propagate online during crisis events. In constructing these signatures, this research demonstrates and documents an emerging method for deeply and recursively integrating qualitative and quantitative methods for analysis of social media trace data. The prevalence of misinformation within social media and online communities can undermine public security and distract attention from important issues. Fact-checking interventions, in which users cite fact-checking websites such as Snopes.com and FactCheck.org, are a strategy users can employ to refute false claims made by their peers. While laboratory research suggests such interventions are not effective in persuading people to abandon false ideas, little work considers how such interventions are actually deployed in real-world conversations. Using approximately 1,600 interventions observed on Twitter between 2012 and 2013, we examine the contexts and consequences of fact-checking interventions.W e focus in particular on the social relationship between the individual who issues the fact-check and the individual whose facts are challenged. Our results indicate that though fact-checking interventions are most commonly issued by strangers, they are more likely to draw user attention and responses when they come from friends. Finally, we discuss implications for designing more effective interventions against misinformation. Retweeting sharing action has enabled information to be cascaded to distant nodes on social network. Unfortunately, malicious users as a group have taken advantage of the retweeting function with coordinated behavior to falsely distort the volume of specific keywords, topics or URLs for promotional purposes (e.g., spreading fake news, and increasing public visibility of products or services). Unfortunately, little is known about their retweeting behavior as a group and how to detect them based on group-based signals. To fill the gap, in this paper, we (i) propose Attractor+ algorithm to extract retweeter groups, members of each of which have similar retweeting behavior; (ii) analyze underlying characteristics of malicious and legitimate retweeter groups; (iii) propose group-based features to catch synchronized and coordinated behavior; and build a predictor to classify if a group is malicious. Experimental results show that our proposed method outperformed existing approaches. We present Emergent, a novel data-set derived from a digital journalism project for rumour debunking. The data-set contains 300 rumoured claims and 2,595 associated news articles, collected and labelled by journalists with an estimation of their veracity (true, false or unverified). Each associated article is summarized into a headline and labelled to indicate whether its stance is for, against, or observing the claim, where observing indicates that the article merely repeats the claim. Thus, Emergent provides a real-world data source for a variety of natural language processing tasks in the context of fact-checking. Further to presenting the dataset, we address the task of determining the article headline stance with respect to the claim. For this purpose we use a logistic regression classifier and develop features that examine the headline and its agreement with the claim. The accuracy achieved was 73 which is 26 higher than the one achieved by the Excitement Open Platform (, 2014). Increasing evidence suggests that a growing amount of social media content is generated by autonomous entities known as social bots. In this work we present a framework to detect such entities on Twitter. We leverage more than a thousand features extracted from public data and meta-data about users: friends, tweet content and sentiment, network patterns, and activity time series. We benchmark the classification framework by using a publicly available dataset of Twitter bots. This training data is enriched by a manually annotated collection of active Twitter users that include both humans and bots of varying sophistication. Our models yield high accuracy and agreement with each other and can detect bots of different nature. Our estimates suggest that between 9 and 15 of active Twitter accounts are bots. Characterizing ties among accounts, we observe that simple bots tend to interact with bots that exhibit more human-like behaviors. Analysis of content flows reveals retweet and mention strategies adopted by bots to interact with different target groups. Using clustering analysis, we characterize several subclasses of accounts, including spammers, self promoters, and accounts that post content from connected applications.