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

Soldner, F; Ho, JC-T; Makhortykh, M; Van der Vegt, I; Mozes, M; Kleinberg, B; (2019) Uphill from here: Sentiment patterns in videos from left- and right-wingYouTube news channels. In: 3rd Workshop on Natural Language Processing and Computational Social Science proceedings. (pp. pp. 84-93). ACL: Minneapolis, MN, USA. Green open access

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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.

Type: Proceedings paper
Title: Uphill from here: Sentiment patterns in videos from left- and right-wingYouTube news channels
Event: 3rd Workshop on Natural Language Processing and Computational Social Science
Location: Minneapolis, USA
Dates: 06 June 2019 - 06 June 2019
ISBN-13: 978-1-950737-04-8
Open access status: An open access version is available from UCL Discovery
Publisher version: https://www.aclweb.org/anthology/W19-2110.pdf
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions - https://www.aclweb.org/anthology/volumes/W19-21/
Keywords: linguistic temporal trajectory analysis, online news, left-wing, right-wing, sentiment analysis, YouTube
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Security and Crime Science
URI: https://discovery.ucl.ac.uk/id/eprint/10073075
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