Detecting anomalies in social network data consumption


As the popularity and usage of social media exploded over the years, understanding how social network users’ interests evolve gained importance in diverse fields, ranging from sociological studies to marketing. In this paper, we use two snapshots from the Twitter network and analyze data interest patterns of users in time to understand individual and collective user behavior on social networks. Building topical profiles of users, we propose novel metrics to identify anomalous friendships, and validate our results with Amazon Mechanical Turk experiments. We show that although more than 80 % of all friendships on Twitter are created due to data interests, 83 % of all users have at least one friendship that can be explained neither by users’ past interest nor collective behavior of other similar users.

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  1. 1.

    In this paper, we use the term “anomaly” to represent such significant changes in user behavior.

  2. 2.

  3. 3.

    In Twitter API, friends of a user are the accounts followed by the user.

  4. 4.

    Two senators are excluded in bioLDA because of short or blank bios.

  5. 5.

    Other words from the topic include words such as green, water, power, wind, oil and gas.

  6. 6.

    The number of new friendships is greater than the total number of queried Twitter users because we have queried Twitter breadth first, and many new friendships are shared by seed users.

  7. 7.

  8. 8.

    Approved by the Office of Research Compliance-University of Texas at Dallas, human experiment IRB MR 13-231.

  9. 9.

    For Fleiss’ Kappa, >0.2 Fair agreement, >0.40 Moderate agreement, >0.6 Substantial agreement


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This work is partially funded by National Science Foundation (NSF) Grants Career—CNS-0845803, CNS-0964350, CNS-1016343, CNS-1111529, CNS-1228198.

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Correspondence to Cuneyt Gurcan Akcora.

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Akcora, C.G., Carminati, B., Ferrari, E. et al. Detecting anomalies in social network data consumption. Soc. Netw. Anal. Min. 4, 231 (2014).

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  • Anomaly Detection
  • Topic Model
  • Latent Dirichlet Allocation
  • Similar User
  • Twitter User