Abstract
News claims that travel the Internet and online social networks (OSNs) originate from different, sometimes unknown sources, which raises issues related to the credibility of those claims and the drivers behind them. Fact-checking websites such as Snopes, FactCheck, and Emergent use human evaluators to investigate and label news claims, but the process is labor- and time-intensive. Driven by the need to use data analytics and algorithms in assessing the credibility of news claims, we focus on what can be generalized about evaluating human-labeled claims. We developed tools to extract claims from Snopes and Emergent and used public datasets collected by and published on those websites. Claims extracted from those datasets were supervised or labeled with different claim ratings. We focus on claims with definite ratings-false, mostly false, true, and mostly true, with the goal of identifying distinctive features that can be used to distinguish true from false claims. Ultimately, those features can be used to predict future unsupervised or unlabeled claims. We evaluate different methods to extract features as well as different sets of features and their ability to predict the correct claim label. By far, we noticed that OSN websites report high rates of false claims in comparison with most of the other website categories. The rate of reported false claims is higher than the rate of true claims in fact-checking websites in most categories. At the content-analysis level, false claims tend to have more negative tones in sentiments and hence can provide supporting features to predict claim classification.
Highlights
With the evolution of the Internet, online social networks (OSNs) dominate how users exchange information
Driven by the need to use data analytics and algorithms in assessing the credibility of news claims, we focus on what can be generalized about evaluating human-labeled claims
We evaluated features related to the contents of the claims and categorized websites based on the nature of how they report true or false claims
Summary
With the evolution of the Internet, online social networks (OSNs) dominate how users exchange information. Current Internet information models do not require content generators to perform any fact-checking. Fact-checking websites such as Snopes, FactCheck, and Emergent represent isolated efforts to deal with information-credibility problems. Those efforts require significant human resources, and some websites (e.g., Emergent) struggle to stay alive. Driven by these challenges in information credibility, we explored the use of data analytics in the information-credibility assessment. We used human efforts in supervised or rated claims, i.e., through websites such as Snopes, FactCheck, and Emergent, to determine what we can learn from claims in terms of predicting their rating based on content and cited websites. We evaluated features related to the contents of the claims and categorized websites based on the nature of how they report true or false claims
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