The 2016 U.S. presidential election has witnessed the major role of Twitter in the year's most important political event. Candidates used this social media platform extensively for online campaigns. Meanwhile, social media has been filled with rumors, which might have had huge impacts on voters' decisions. In this paper, we present a thorough analysis of rumor tweets from the followers of two presidential candidates: Hillary Clinton and Donald Trump. To overcome the difficulty of labeling a large amount of tweets as training data, we detect rumor tweets by matching them with verified rumor articles. We analyze over 8 million tweets collected from the followers of the two candidates. Our results provide answers to several primary concerns about rumors in this election, including: which side of the followers posted the most rumors, who posted these rumors, what rumors they posted, and when they posted these rumors. The insights of this paper can help us understand the online rumor behaviors in American politics.
Though Twitter acts as a realtime news source with people acting as sensors and sending event updates from all over the world, rumors spread via Twitter have been noted to cause considerable damage. Given a set of popular Twitter events along with related users and tweets, we study the problem of automatically assessing the credibility of such events. We propose a credibility analysis approach enhanced with event graph-based optimization to solve the problem. First we experiment by performing PageRanklike credibility propagation on a multi-typed network consisting of events, tweets, and users. Further, within each iteration, we enhance the basic trust analysis by updating event credibility scores using regularization on a new graph of events. Our experiments using events extracted from two tweet feed datasets, each with millions of tweets show that our event graph optimization approach outperforms the basic credibility analysis approach. Also, our methods are significantly more accurate (∼86 ) than the decision tree classifier approach (∼72 ). Benefiting from its openness, collaboration and real-time features, Micro blog has become one of the most important news communication media in modern society. However, it is also filled with fake news. Without verification, such information could spread promptly through social network and result in serious consequences. To evaluate news credibility on Micro blog, we propose a hierarchical propagation model. We detect sub-events within a news event to describe its detailed aspects. Thus, for a news event, a three-layer credibility network consisting of event, sub-events and messages can represent it from different scale and reveal vital information for credibility evaluation. After linking these entities with their semantic and social associations, the credibility value of each entity is propagated on this network to achieve the final evaluation result. By formulating this propagation process as a graph optimization problem, we provide a globally optimal solution with an iterative algorithm. Experiments conducted on two real-world datasets show that the proposed model boosts the accuracy by more than 6 and the F-score by more than 16 over a baseline method. Microblog has been a popular media platform for reporting and propagating news. However, fake news spreading on microblogs would severely jeopardize its public credibility. To identify the truthfulness of news on microblogs, images are very crucial content. In this paper, we explore the key role of image content in the task of automatic news verification on microblogs. Existing approaches to news verification depend on features extracted mainly from the text content of news tweets, while image features for news verification are often ignored. According to our study, however, images are very popular and have a great influence on microblogs news propagation. In addition, fake and real news events have different image distribution patterns. Therefore, we propose several visual and statistical features to characterize these patterns visually and statistically for detecting fake news. Experiments on a real-world multimedia dataset collected from Sina Weibo validate the effectiveness of our proposed image features. The news verification performance of our method outperforms baseline methods. To the best of our knowledge, this is the first attempt that systematically explores image features on news verification task. We analyze the information credibility of news propagated through Twitter, a popular microblogging service. Previous research has shown that most of the messages posted on Twitter are truthful, but the service is also used to spread misinformation and false rumors, often unintentionally. On this paper we focus on automatic methods for assessing the credibility of a given set of tweets. Specifically, we analyze microblog postings related to "trending" topics, and classify them as credible or not credible, based on features extracted from them. We use features from users' posting and re-posting ("re-tweeting") behavior, from the text of the posts, and from citations to external sources. We evaluate our methods using a significant number of human assessments about the credibility of items on a recent sample of Twitter postings. Our results shows that there are measurable differences in the way messages propagate, that can be used to classify them automatically as credible or not credible, with precision and recall in the range of 70 to 80 . This paper studies the problem of automatic detection of false rumors on Sina Weibo, the popular Chinese microblogging social network. Traditional feature-based approaches extract features from the false rumor message, its author, as well as the statistics of its responses to form a flat feature vector. This ignores the propagation structure of the messages and has not achieved very good results. We propose a graph-kernel based hybrid SVM classifier which captures the high-order propagation patterns in addition to semantic features such as topics and sentiments. The new model achieves a classification accuracy of 91.3 on randomly selected Weibo dataset, significantly higher than state-of-the-art approaches. Moreover, our approach can be applied at the early stage of rumor propagation and is 88 confident in detecting an average false rumor just 24 hours after the initial broadcast.  Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day. This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment. Using LIWC text analysis software, we conducted a content-analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is indeed used extensively for political deliberation. We find that the mere number of messages mentioning a party reflects the election result. Moreover, joint mentions of two parties are in line with real world political ties and coalitions. An analysis of the tweets’ political sentiment demonstrates close correspondence to the parties' and politicians’ political positions indicating that the content of Twitter messages plausibly reflects the offline political landscape. We discuss the use of microblogging message content as a valid indicator of political sentiment and derive suggestions for further research.