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Uncovering Topic Dynamics of Social Media and News: The Case of Ferguson

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Social Informatics (SocInfo 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10046))

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Abstract

Looking at the dynamics of news content and social media content can help us understand the increasingly complex dynamics of the relationship between the media and the public surrounding noteworthy news events. Although topic models such as latent Dirichlet allocation (lda) are valuable tools, they are a poor fit for analyses in which some documents, like news articles, tend to incorporate multiple topics, while others, like tweets, tend to be focused on just one. In this paper, we propose Single Topic lda (st-lda) which jointly models news-type documents as distributions of topics and tweets as having a single topic; the model improves topic discovery in news and tweets within a unified topic space by removing noisy topics that conventional lda tends to assign to tweets. Using st-lda, we focus on the unrest in Ferguson, Missouri after the fatal shooting of Michael Brown on August 9, 2014, looking in particular at the topic dynamics of tweets in and out of St. Louis area, and at differences and relationships between topic coverage in news and tweets.

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Notes

  1. 1.

    To identify locations of tweets, we refer to the geographic boundary file of 2014 TIGER/Line, https://www.census.gov/geo/maps-data/data/tiger-line.html.

  2. 2.

    Tweets from these media sources are filtered from our Twitter data.

  3. 3.

    News tokenization is done by OpenNLP, https://opennlp.apache.org/. Tweet tokenization is done by Twokenizer, http://www.cs.cmu.edu/~ark/TweetNLP/.

  4. 4.

    Code is available at https://github.com/ywwbill/YWWTools#st_lda_cmd.

  5. 5.

    Note that st-lda will not outperform lda on perplexity, since the words in a tweet are generated from the same topic. However, the sacrifice of perplexity brings improvement in topic identification.

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Acknowledgement

We thank anonymous reviewers for their insightful comments.

Author information

Authors and Affiliations

  1. College of Information Studies, University of Maryland, College Park, Maryland, USA

    Lingzi Hong & Vanessa Frias-Martinez

  2. Department of Computer Science, University of Maryland, College Park, Maryland, USA

    Weiwei Yang

  3. Department of Linguistics, University of Maryland, College Park, Maryland, USA

    Philip Resnik

  4. Institute for Advanced Computer Studies, University of Maryland, College Park, Maryland, USA

    Philip Resnik & Vanessa Frias-Martinez

Authors
  1. Lingzi Hong
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  2. Weiwei Yang
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  3. Philip Resnik
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  4. Vanessa Frias-Martinez
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Corresponding author

Correspondence to Lingzi Hong .

Editor information

Editors and Affiliations

  1. University of Washington, Seattle, Washington, USA

    Emma Spiro

  2. Indiana University, Bloomington, Indiana, USA

    Yong-Yeol Ahn

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Hong, L., Yang, W., Resnik, P., Frias-Martinez, V. (2016). Uncovering Topic Dynamics of Social Media and News: The Case of Ferguson. In: Spiro, E., Ahn, YY. (eds) Social Informatics. SocInfo 2016. Lecture Notes in Computer Science(), vol 10046. Springer, Cham. https://doi.org/10.1007/978-3-319-47880-7_15

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  • DOI: https://doi.org/10.1007/978-3-319-47880-7_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47879-1

  • Online ISBN: 978-3-319-47880-7

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