Abstract
Toxic behaviors such as aggression, harassment, and insults are the scourge of social media and online discussion sites. Conflictual interactions of this type finally lead dialogues off the rails and as a result the dialogue loses its essence. Preventing a discussion from such incidents requires the ability to predict such situation. This, in turn, presents the challenge of modeling and understanding the dynamics of dialogues and thus predefining the factors that may change throughout their course.
In this work we propose to take an emotional intensity of discussion posts as such factor and to model an emotional trajectory of the dialogue. Therefore we also present an approach for modeling the dynamics using time series representation of dialogue. Finally we perform an experiment of time series forecasting on collection of such conversational time series with state-of-the-art deep learning model in order to make a prediction about emotional intensity value of upcoming post.
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Notes
- 1.
notebooks and preprocessed collections are publicly available: https://github.com/mmarcinowski/Emotions-Intensity-Prediction.
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Marcinowski, M. (2023). Emotions Intensity Prediction in Online Discussions Using Time Series Forecasting. In: Garrigós, I., Murillo Rodríguez, J.M., Wimmer, M. (eds) Web Engineering. ICWE 2023. Lecture Notes in Computer Science, vol 13893. Springer, Cham. https://doi.org/10.1007/978-3-031-34444-2_13
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