Social media has become a popular means for people to consume news. Meanwhile, it also enables the wide dissemination of fake news, i.e., news with intentionally false information, which brings significant negative effects to the society. Thus, fake news detection is attracting increasing attention. However, fake news detection is a non-trivial task, which requires multi-source information such as news content, social context, and dynamic information. First, fake news is written to fool people, which makes it difficult to detect fake news simply based on news contents. In addition to news contents, we need to explore social contexts such as user engagements and social behaviors. For example, a credible user's comment that "this is a fake news" is a strong signal for detecting fake news. Second, dynamic information such as how fake news and true news propagate and how users' opinions toward news pieces are very important for extracting useful patterns for (early) fake news detection and intervention. Thus, comprehensive datasets which contain news content, social context, and dynamic information could facilitate fake news propagation, detection, and mitigation; while to the best of our knowledge, existing datasets only contains one or two aspects. Therefore, in this paper, to facilitate fake news related researches, we provide a fake news data repository FakeNewsNet, which contains two comprehensive datasets that includes news content, social context, and dynamic information. We present a comprehensive description of datasets collection, demonstrate an exploratory analysis of this data repository from different perspectives, and discuss the benefits of FakeNewsNet for potential applications on fake news study on social media.
http: compsocial.github.io CREDBANK-data : This is a large-scale crowd-sourced dataset @cite_15 of around 60 million tweets that cover 96 days starting from Oct. 2015. The tweets are related to over 1,000 news events. Each event is assessed for credibilities by 30 annotators from Amazon Mechanical Turk.
@cite_15
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Social media has quickly risen to prominence as a news source, yet lingering doubts remain about its ability to spread rumor and misinformation. Systematically studying this phenomenon, however, has been difficult due to the need to collect large-scale, unbiased data along with in-situ judgements of its accuracy. In this paper we present CREDBANK, a corpus designed to bridge this gap by systematically combining machine and human computation. Specifically, CREDBANK is a corpus of tweets, topics, events and associated human credibility judgements. It is based on the real-time tracking of more than 1 billion streaming tweets over a period of more than three months, computational summarizations of those tweets, and intelligent routings of the tweet streams to human annotators — within a few hours of those events unfolding on Twitter. In total CREDBANK comprises more than 60 million tweets grouped into 1049 real-world events, each annotated by 30 human annotators. As an example, with CREDBANK one can quickly calculate that roughly 24 of the events in the global tweet stream are not perceived as credible. We have made CREDBANK publicly available, and hope it will enable new research questions related to online information credibility in fields such as social science, data mining and health. We present two new NER datasets for Twitter; a manually annotated set of 1,467 tweets (κ = 0.942) and a set of 2,975 expert-corrected, crowdsourced NER annotated tweets from the dataset described in (2010). In our experiments with these datasets, we observe two important points: (a) language drift on Twitter is significant, and while off-the-shelf systems have been reported to perform well on in-sample data, they often perform poorly on new samples of tweets, (b) state-of-the-art performance across various datasets can be obtained from crowdsourced annotations, making it more feasible to ”catch up” with language drift. Twitter, as a popular microblogging service, has become a new information channel for users to receive and exchange the most up-to-date information on current events. However, since there is no control on how users can publish messages on Twitter, finding newsworthy events from Twitter becomes a difficult task like "finding a needle in a haystack". In this paper we propose a general unsupervised framework to explore events from tweets, which consists of a pipeline process of filtering, extraction and categorization. To filter out noisy tweets, the filtering step exploits a lexicon-based approach to separate tweets that are event-related from those that are not. Then, based on these event-related tweets, the structured representations of events are extracted and categorized automatically using an unsupervised Bayesian model without the use of any labelled data. Moreover, the categorized events are assigned with the event type labels without human intervention. The proposed framework has been evaluated on over 60 millions tweets which were collected for one month in December 2010. A precision of 70.49 is achieved in event extraction, outperforming a competitive baseline by nearly 6 . Events are also clustered into coherence groups with the automatically assigned event type label. The CrisisMMD multimodal Twitter dataset consists of several thousands of manually annotated tweets and images collected during seven major natural disasters including earthquakes, hurricanes, wildfires, and floods that happened in the year 2017 across different parts of the World. The provided datasets include three types of annotations. Informative vs not-informative: Informative Not informative Don’t know or can’t judge Humanitarian categories Affected individuals Infrastructure and utility damage Injured or dead people Missing or found people Rescue, volunteering or donation effort Vehicle damage Other relevant information Not relevant or can’t judge Damage severity assessment Severe damage Mild damage Little or no damage Don’t know or can’t judge Please use alternate download link CrisisNLP, in case if you are having problem in downloading from this site. You can also get tweet ids and a tweet downloader tool too.