Rumor Detection

A rumor is commonly defined as a state- ment whose true value is unverifiable. Ru- mors may spread misinformation (false infor- mation) or disinformation (deliberately false information) on a network …...

AbstractGiven the constantly growing proliferation of false claims online in recent years, there has been also a growing research interest in automatically distinguishing false rumors from factually true claims. Here, …...

AbstractIn this study, we propose a new multi-task learning approach for rumor detection and stance classification tasks. This neural network model has a shared layer and two task specific layers. …...

AbstractLearning from social-media conversations has gained significant attention recently because of its applications in areas like rumor detection. In this research, we propose a new way to represent social-media conversations …...

AbstractRumors are manufactured with no respect for accuracy, but can circulate quickly and widely by “word-of-post” through social media conversations. Conversation tree encodes important information indicative of the credibility of …...

AbstractAs one of the most popular social media platforms, Twitter has become a primary source of information for many people. Unfortunately, both valid information and rumors are propagated on Twitter …...

AbstractSocial media platforms such as Twitter have become a breeding ground for unverified information or rumors. These rumors can threaten people’s health, endanger the economy, and affect the stability of …...

With the rapid growth of information, browsing social media on the Internet is becoming a part of people’s daily lives. Social platforms give us the latest information in real time, …...

While social media becomes a primary source of news now, it also becomes more challeng- ing for people to distinguish the rumors and non-rumors, which attracts malicious manip- ulation and …...

AbstractThere has been much interest in rumor detection using deep learning models in recent years. A well-known limitation of deep learning models is that they tend to learn superficial patterns, …...

AbstractAs the sources of information that we consume everyday rapidly diversify, it is becoming increasingly important to develop NLP tools that help to evaluate the credibility of the information we …...

Existing rumor detection strategies typically provide detection labels while ignoring their explanation. Nonetheless, providing pieces of evidence to explain why a suspicious tweet is rumor is essential. As such, a …...

AbstractRumors are rampant in the era of social media. Conversation structures provide valuable clues to differentiate between real and fake claims. However, existing rumor detection methods are either limited to …...

AbstractSocial networks face a major challenge in the form of rumors and fake news, due to their intrinsic nature of connecting users to millions of others, and of giving any …...

AbstractRumor detection on social media puts pre-trained language models (LMs), such as BERT, and auxiliary features, such as comments, into use. However, on the one hand, rumor detection datasets in …...

AbstractDetecting rumors on social media is a very critical task with significant implications to the economy, public health, etc. Previous works generally capture effective features from texts and the propagation …...

AbstractNew events emerge over time influencing the topics of rumors in social media. Current rumor detection benchmarks use random splits as training, development and test sets which typically results in …...