Telling Apart Tweets Associated with Controversial versus Non-Controversial Topics

Aseel Addawood, Rezvaneh Rezapour, Omid Abdar, Jana Diesner


Abstract
In this paper, we evaluate the predictability of tweets associated with controversial versus non-controversial topics. As a first step, we crowd-sourced the scoring of a predefined set of topics on a Likert scale from non-controversial to controversial. Our feature set entails and goes beyond sentiment features, e.g., by leveraging empathic language and other features that have been previously used but are new for this particular study. We find focusing on the structural characteristics of tweets to be beneficial for this task. Using a combination of emphatic, language-specific, and Twitter-specific features for supervised learning resulted in 87% accuracy (F1) for cross-validation of the training set and 63.4% accuracy when using the test set. Our analysis shows that features specific to Twitter or social media, in general, are more prevalent in tweets on controversial topics than in non-controversial ones. To test the premise of the paper, we conducted two additional sets of experiments, which led to mixed results. This finding will inform our future investigations into the relationship between language use on social media and the perceived controversiality of topics.
Anthology ID:
W17-2905
Volume:
Proceedings of the Second Workshop on NLP and Computational Social Science
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
NLP+CSS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
32–41
Language:
URL:
https://aclanthology.org/W17-2905
DOI:
10.18653/v1/W17-2905
Bibkey:
Cite (ACL):
Aseel Addawood, Rezvaneh Rezapour, Omid Abdar, and Jana Diesner. 2017. Telling Apart Tweets Associated with Controversial versus Non-Controversial Topics. In Proceedings of the Second Workshop on NLP and Computational Social Science, pages 32–41, Vancouver, Canada. Association for Computational Linguistics.
Cite (Informal):
Telling Apart Tweets Associated with Controversial versus Non-Controversial Topics (Addawood et al., NLP+CSS 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-2905.pdf