@inproceedings{zuo2018hybrid,
  author={Zuo, Chaoyuan and Karakas, Ayla and Banerjee, Ritwik},
  title={{A Hybrid Recognition System for Check-worthy Claims Using Heuristics and Supervised Learning}},
  booktitle = {CLEF 2018 Working Notes. Working Notes of CLEF 2018 - Conference and Labs of the Evaluation Forum},
    series    = {{CEUR} Workshop Proceedings},
    publisher = {CEUR-WS.org},
    editor    = {Cappellato, Linda and Ferro, Nicola and Nie, Jian-Yun and Soulier, Laure},
    address   = {Avignon, France},
    month     = {September},
    year      = {2018}
}

In recent years, the speed at which information disseminates has received an alarming boost from the pervasive usage of social media. To the detriment of political and social stability, this has also made it easier to quickly spread false claims. Due to the sheer volume of information, manual fact-checking seems infeasible, and as a result, computational approaches have been recently explored for automated fact-checking. In spite of the recent advancements in this direction, the critical step of recognizing and prioritizing statements worth fact-checking has received little attention. In this paper, we propose a hybrid approach that combines simple heuristics with supervised machine learning to identify claims made in political debates and speeches, and provide a mechanism to rank them in terms of their "check-worthiness". The viability of our method is demonstrated by evaluations on the English language dataset as part of the Check-worthiness task of the CLEF-2018 Fact Checking Lab.

@inproceedings{zuo2019check,
  author      = {Zuo, Chaoyuan and Karakas, Ayla and Banerjee, Ritwik},
  title       = {{To Check or not to Check: Syntax, Semantics, and Context in the Language of Check-worthy Claims}},
  editor      = {Crestani, Fabio and Braschler, Martin and Savoy, Jacques and Rauber, Andreas and Müller, Henning
                 and Losada, David E. and Bürki, Gundula H. and Cappellato, Linda and Ferro, Nicola},
  booktitle   = {Experimental IR Meets Multilinguality, Multimodality, and Interaction – Proceedings of the
                 10th International Conference of the CLEF Association},
  publisher   = {Springer International Publishing},
  year        = {2019},
  series      = {Lecture Notes in Computer Science},
  volume      = {11696}
}

As the spread of information has received a compelling boost due to pervasive use of social media, so has the spread of misinformation. The sheer volume of data has rendered the traditional methods of expert-driven manual fact-checking largely infeasible. As a result, computational linguistics and data-driven algorithms have been explored in recent years. Despite this progress, identifying and prioritizing what needs to be checked has received little attention. Given that expert-driven manual intervention is likely to remain an important component of fact-checking, especially in specific domains (e.g., politics, environmental science), this identification and prioritization is critical. A successful algorithmic ranking of "check-worthy" claims can help an expert-in-the-loop fact-checking system, thereby reducing the expert's workload while still tackling the most salient bits of misinformation. In this work, we explore how linguistic syntax, semantics, and the contextual meaning of words play a role in determining the check-worthiness of claims. Our preliminary experiments used explicit stylometric features and simple word embeddings on the English language dataset in the Check-worthiness task of the CLEF-2018 Fact-Checking Lab, where our primary solution outperformed the other systems in terms of the mean average precision, R-precision, reciprocal rank, and precision at k for multiple values of k. Here, we present an extension of this approach with more sophisticated word embeddings and report further improvements in this task.