Felix Soldner


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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
Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science

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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Box of Lies: Multimodal Deception Detection in Dialogues
Felix Soldner | Verónica Pérez-Rosas | Rada Mihalcea
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Deception often takes place during everyday conversations, yet conversational dialogues remain largely unexplored by current work on automatic deception detection. In this paper, we address the task of detecting multimodal deceptive cues during conversational dialogues. We introduce a multimodal dataset containing deceptive conversations between participants playing the Box of Lies game from The Tonight Show Starring Jimmy Fallon, in which they try to guess whether an object description provided by their opponent is deceptive or not. We conduct annotations of multimodal communication behaviors, including facial and linguistic behaviors, and derive several learning features based on these annotations. Initial classification experiments show promising results, performing well above both a random and a human baseline, and reaching up to 69% accuracy in distinguishing deceptive and truthful behaviors.