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IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

ASONAM 2018: Influence and Behavior Analysis in Social Networks and Social Media pp 63–83Cite as

Emotional Valence Shifts and User Behavior on Twitter, Facebook, and YouTube

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Part of the book series: Lecture Notes in Social Networks ((LNSN))

Abstract

In this paper, we present a study on 5.6 million messages that have been sent via Twitter, Facebook, and YouTube. The messages in our data set are related to 24 systematically chosen real-world events. For each of the 5.6 million messages, we first extracted emotion scores based on the eight basic emotions according to Plutchik’s wheel of emotions. Subsequently, we investigated the effects of shifts in the emotional valence on the messaging behavior of social media users. In particular, we found empirical evidence that prospectively negative real-world events exhibit a significant amount of shifted (i.e., positive) emotions in the corresponding messages. To explain this finding, we use the theory of social connection and emotional contagion. To the best of our knowledge, this is the first study that provides empirical evidence for the undoing hypothesis in online social networks (OSNs). The undoing hypothesis assumes that positive emotions serve as an antidote during negative events.

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Notes

  1. 1.

    https://dev.twitter.com/overview/api.

  2. 2.

    https://github.com/philbot9/youtube-comment-api.

  3. 3.

    https://developers.facebook.com/docs/graph-api.

  4. 4.

    https://pypi.python.org/pypi/langdetect.

  5. 5.

    The AFINN lexicon [31] contains scores corresponding to the emotional valence intensity of a given word. For example, words such as sad and depressed are classified as negative words, but the latter has a weaker intensity compared to the former word.

  6. 6.

    Note that the increased limit of 280 characters that has been introduced by Twitter in November 2017 was not in effect during our data extraction period.

  7. 7.

    Set difference refers to the difference between the count of the expected emotions and shifted emotions, while sd stands for standard deviation.

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Acknowledgements

Mauro Conti is supported by a Marie Curie Fellowship funded by the European Commission (agreement PCIG11-GA-2012-321980). This work is also partially supported by the EU TagItSmart! Project (agreement H2020-ICT30-2015-688061), the EU-India REACH Project (agreement ICI+/2014/342-896), the project CNR-MOST/Taiwan 2016-17 “Verifiable Data Structure Streaming,” the grant n. 2017-166478 (3696) from Cisco University Research Program Fund and Silicon Valley Community Foundation, and the grant “Scalable IoT Management and Key security aspects in 5G systems” from Intel.

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Authors and Affiliations

  1. Vienna University of Economics and Business, Wien, Austria

    Ema Kušen & Mark Strembeck

  2. Secure Business Austria Research Center (SBA), Wien, Austria

    Mark Strembeck

  3. Complexity Science Hub Vienna (CSH), Wien, Austria

    Mark Strembeck

  4. Università di Padova, Padua, Italy

    Mauro Conti

Authors
  1. Ema Kušen
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  2. Mark Strembeck
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  3. Mauro Conti
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Correspondence to Mark Strembeck .

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Editors and Affiliations

  1. Department of Computer Engineering, Firat University, Elazig, Türkiye

    Mehmet Kaya

  2. Department of Computer Science, University of Calgary, Calgary, AB, Canada

    Reda Alhajj

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Kušen, E., Strembeck, M., Conti, M. (2019). Emotional Valence Shifts and User Behavior on Twitter, Facebook, and YouTube. In: Kaya, M., Alhajj, R. (eds) Influence and Behavior Analysis in Social Networks and Social Media. ASONAM 2018. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-030-02592-2_4

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  • DOI: https://doi.org/10.1007/978-3-030-02592-2_4

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