Explaining the Credibility of Claims on the Web and Social Media

The web is a huge source of valuable information. However, in recent times, there is an increasing trend towards false claims in social media, other web-sources, and even in news. Thus, fact-checking websites have become increasingly popular to identify such misinformation based on manual analysis. Recent research proposed methods to assess the credibility of claims automatically. However, there are major limitations: most works assume claims to be in a structured form, and a few deal with textual claims but require that sources of evidence or counter-evidence are easily retrieved from the web. None of these works can cope with newly emerging claims, and no prior method can give user-interpretable explanations for its verdict on the claim’s credibility.

These papers overcome these limitations by automatically assessing the credibility of emerging claims, with sparse presence in web-sources, and generating suitable explanations from judiciously selected sources. To this end, we retrieve diverse articles about the claim, and model the mutual interaction between: the stance (i.e., support or refute) of the sources, the language style of the articles, the reliability of the sources, and the claim’s temporal footprint on the web. Extensive experiments demonstrate the viability of our method and its superiority over prior works. We show that our methods work well for early detection of emerging claims, as well as for claims with limited presence on the web and social media.

Publications

  • Kashyap Popat, Subhabrata Mukherjee, Jannik Strötgen and Gerhard Weikum.
    Where the Truth Lies: Explaining the Credibility of Emerging Claims on the Web and Social Media
    Proc. of the 26th International World Wide Web Conference (WWW), 2017.
    PDF BIB
  • Kashyap Popat, Subhabrata Mukherjee, Jannik Strötgen and Gerhard Weikum.
    Credibility Assessment of Textual Claims on the Web
    Proc. of the 25th ACM Conference on Information and Knowledge Management (CIKM), 2016.
    PDF BIB

Downloads

Datasets used in the publications:

  1. Snopes dataset (download)
  2. Wikipedia dataset (download)