Kashyap Popat

Where the Truth Lies: Explaining the Credibility of Emerging 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, factchecking 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 claims credibility. This paper overcomes 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 claims 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.

Credibility Assessment of Textual Claims on the Web

There is an increasing amount of false claims in news, social media, and other web sources. While prior work on truth discovery has focused on the case of checking factual statements, this paper addresses the novel task of assessing the credibility of arbitrary claims made in natural-language text - in an open-domain setting without any assumptions about the structure of the claim, or the community where it is made. Our solution is based on automatically finding sources in news and social media, and feeding these into a distantly supervised classifier for assessing the credibility of a claim (i.e., true or fake). For inference, our method leverages the joint interaction between the language of articles about the claim and the reliability of the underlying web sources. Experiments with claims from the popular website snopes.com and from reported cases of Wikipedia hoaxes demonstrate the viability of our methods and their superior accuracy over various baselines.

Exploring Latent Semantic Factors to Find Useful Product Reviews

Online reviews provided by consumers are a valuable asset for e-Commerce platforms, influencing potential consumers in making purchasing decisions. However, these reviews are of varying quality, with the useful ones buried deep within a heap of non-informative reviews. In this work, we attempt to automatically identify review quality in terms of its helpfulness to the end consumers. In contrast to previous works in this domain exploiting a variety of syntactic and community-level features, we delve deep into the semantics of reviews as to what makes them useful, providing interpretable explanation for the same. We identify a set of consistency and semantic factors, all from the text, ratings, and timestamps of user-generated reviews, making our approach generalizable across all communities and domains. We explore review semantics in terms of several latent factors like the expertise of its author, his judgment about the fine-grained facets of the underlying product, and his writing style. These are cast into a Hidden Markov Model -- Latent Dirichlet Allocation (HMM-LDA) based model to jointly infer: (i) reviewer expertise, (ii) item facets, and (iii) review helpfulness. Large-scale experiments on five real-world datasets from Amazon show significant improvement over state-of-the-art baselines in predicting and ranking useful reviews.

CredEye: A Credibility Lens for Analyzing and Explaining Misinformation

Rapid increase of misinformation online has emerged as one of the biggest challenges in this post-truth era. This has given rise to many fact-checking websites that manually assess doubtful claims. However, the speed and scale at which misinformation spreads in online media inherently limits manual verification. Hence, the problem of automatic credibility assessment has attracted great attention. In this work, we present CredEye, a system for automatic credibility assessment. It takes a natural language claim as input from the user and automatically analyzes its credibility by considering relevant articles from the Web. Our system captures joint interaction between language style of articles, their stance towards a claim and the trustworthiness of the sources. In addition, extraction of supporting evidence in the form of enriched snippets makes the verdicts of CredEye transparent and interpretable.

Interpretable credibility assessment of web claims by Kashyap Popat with Prateek Jain as coordinator

Kashyap Popat is a PhD candidate, advised by Prof. Dr. Gerhard Weikum, at the Max Planck Institute for Informatics (MPI-INF) and the University of Saarland in Saarbrücken, Germany. His PhD thesis focuses on analyzing and explaining credibility of textual content. His research interests span text mining, natural language processing and deep learning. Prior to joining MPI-INF, he completed his masters from Indian Institute of Technology Bombay (IIT Bombay) and worked at IBM Research India. He has co-authored several publications in different areas of natural language processing and text mining. For more information, please visit his website at: http://mpi-inf.mpg.de/kpopat

Assessing the Credibility of Claims on the Web

In my doctoral research, I plan to address the problem of assessing the credibility of arbitrary claims made in natural-language text - in an open-domain setting. Automatic credibility assessment is a complex task depending upon many factors. To start with, we propose three factors which can help in assessing the credibility of textual claims: (i) the reliability of the web sources talking about the claim, (ii) the language style of the articles reporting the claim and, (iii) their stance (i.e., support or refute) towards the claim. In addition, we also focus on extracting user-interpretable explanations as evidence supporting the verdict of the assessment.