@inproceedings{soldner-etal-2019-uphill,
title = "Uphill from here: Sentiment patterns in videos from left- and right-wing {Y}ou{T}ube news channels",
author = "Soldner, Felix and
Ho, Justin Chun-ting and
Makhortykh, Mykola and
van der Vegt, Isabelle W.J. and
Mozes, Maximilian and
Kleinberg, Bennett",
editor = "Volkova, Svitlana and
Jurgens, David and
Hovy, Dirk and
Bamman, David and
Tsur, Oren",
booktitle = "Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-2110",
doi = "10.18653/v1/W19-2110",
pages = "84--93",
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.",
}
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%0 Conference Proceedings
%T Uphill from here: Sentiment patterns in videos from left- and right-wing YouTube news channels
%A Soldner, Felix
%A Ho, Justin Chun-ting
%A Makhortykh, Mykola
%A van der Vegt, Isabelle W.J.
%A Mozes, Maximilian
%A Kleinberg, Bennett
%Y Volkova, Svitlana
%Y Jurgens, David
%Y Hovy, Dirk
%Y Bamman, David
%Y Tsur, Oren
%S Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F soldner-etal-2019-uphill
%X 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.
%R 10.18653/v1/W19-2110
%U https://aclanthology.org/W19-2110
%U https://doi.org/10.18653/v1/W19-2110
%P 84-93
Markdown (Informal)
[Uphill from here: Sentiment patterns in videos from left- and right-wing YouTube news channels](https://aclanthology.org/W19-2110) (Soldner et al., NLP+CSS 2019)
ACL