Uphill from here: Sentiment patterns in videos from left- and right-wing YouTube news channels

Felix Soldner, Justin Chun-ting Ho, Mykola Makhortykh, Isabelle W.J. van der Vegt, Maximilian Mozes, Bennett Kleinberg


Abstract
News consumption exhibits an increasing shift towards online sources, which bring platforms such as YouTube more into focus. Thus, the distribution of politically loaded news is easier, receives more attention, but also raises the concern of forming isolated ideological communities. Understanding how such news is communicated and received is becoming increasingly important. To expand our understanding in this domain, we apply a linguistic temporal trajectory analysis to analyze sentiment patterns in English-language videos from news channels on YouTube. We examine transcripts from videos distributed through eight channels with pro-left and pro-right political leanings. Using unsupervised clustering, we identify seven different sentiment patterns in the transcripts. We found that the use of two sentiment patterns differed significantly depending on political leaning. Furthermore, we used predictive models to examine how different sentiment patterns relate to video popularity and if they differ depending on the channel’s political leaning. No clear relations between sentiment patterns and popularity were found. However, results indicate, that videos from pro-right news channels are more popular and that a negative sentiment further increases that popularity, when sentiments are averaged for each video.
Anthology ID:
W19-2110
Volume:
Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Editors:
Svitlana Volkova, David Jurgens, Dirk Hovy, David Bamman, Oren Tsur
Venue:
NLP+CSS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
84–93
Language:
URL:
https://aclanthology.org/W19-2110
DOI:
10.18653/v1/W19-2110
Bibkey:
Cite (ACL):
Felix Soldner, Justin Chun-ting Ho, Mykola Makhortykh, Isabelle W.J. van der Vegt, Maximilian Mozes, and Bennett Kleinberg. 2019. Uphill from here: Sentiment patterns in videos from left- and right-wing YouTube news channels. In Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science, pages 84–93, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Uphill from here: Sentiment patterns in videos from left- and right-wing YouTube news channels (Soldner et al., NLP+CSS 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-2110.pdf