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Assessing the Effects of Social Familiarity and Stance Similarity in Interaction Dynamics

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Complex Networks & Their Applications VI (COMPLEX NETWORKS 2017)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 689))

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Abstract

Homophily, the phenomenon of similar people getting connected to and being socially familiar with each other, is well-known on online social networks. Detection of user stance towards given topics, on online social networks, specifically Twitter, has emerged as a mainstream research topic. The current work provides insights into the impact of topic-specific stance similarity and social familiarity on social interaction dynamics. This is a novel and yet fundamental problem in social networks research, that has so far remained unexplored in the literature. Specifically, we address two key aspects. One, we investigate whether the smoothness (politeness) level of conversations between user pairs, relate with overall stance similarity (spanning across topics). Two, we examine the impact on interaction smoothness (politeness) with respect to social familiarity and topical stance-similarity. We propose a novel approach based on word embedding, to compare across users and across topics. We analyze the relationship between topical stance similarity, social familiarity and interaction politeness of users, with respect to specific familiarities between user pairs as well as social communities.

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Notes

  1. 1.

    https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing.

  2. 2.

    https://snap.stanford.edu/data/twitter7.html.

  3. 3.

    http://an.kaist.ac.kr/traces/WWW2010.html.

  4. 4.

    https://github.com/sudhof/politeness.

  5. 5.

    https://github.com/stanfordnlp/CoreNLP/blob/master/data/edu/stanford/nlp/patterns/surface/stopwords.txt.

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Author information

Authors and Affiliations

  1. IBM Research, New Delhi, India

    Kuntal Dey

  2. Netaji Subhas Institute of Technology, New Delhi, India

    Ritvik Shrivastava & Vaibhav Mathur

  3. Indian Institute of Technology, Delhi, India

    Saroj Kaushik

Authors
  1. Kuntal Dey
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  2. Ritvik Shrivastava
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  3. Saroj Kaushik
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  4. Vaibhav Mathur
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Corresponding author

Correspondence to Kuntal Dey .

Editor information

Editors and Affiliations

  1. University of Lyon 2, Lyon, France

    Chantal Cherifi

  2. University of Burgundy, Dijon, France

    Hocine Cherifi

  3. École Normale Supérieure de Lyon, Lyon, France

    Márton Karsai

  4. University College London, London, United Kingdom

    Mirco Musolesi

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Dey, K., Shrivastava, R., Kaushik, S., Mathur, V. (2018). Assessing the Effects of Social Familiarity and Stance Similarity in Interaction Dynamics. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_68

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  • DOI: https://doi.org/10.1007/978-3-319-72150-7_68

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  • Online ISBN: 978-3-319-72150-7

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