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A Solution to Tweet-Based User Identification Across Online Social Networks

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Advanced Data Mining and Applications (ADMA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10604))

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Abstract

User identification can help us build better users’ profiles and benefit many applications. It has attracted many scholars’ attention. The existing works with good performance are mainly based on the rich online data. However, due to the privacy settings, it is costless or even difficult to obtain the rich data. Besides some profile attributes do not require exclusivity and are easily faked by users for different purposes. This makes the existing schemes are quite fragile. Users often publicly publish their activities on different social networks. This provides a way to overcome the above problem. We aim to address the user identification only based on users’ tweets. We first formulate the user identification based on tweets and propose a tweet-based user identification model. Then a supervised machine learning based solution is presented. It consists of three key steps: first, we propose several algorithms to measure the spatial similarity, temporal similarity and content similarity of two tweets; second, we extract the spatial, temporal and content features to exploit information redundancies; Afterwards, we employ the machine learning method for user identification. The experiment shows that the proposed solution can provide excellent performance with F1 values reaching 89.79%, 86.78% and 86.24% on three ground truth datasets, respectively. This work shows the possibility of user identification with easily accessible and not easily impersonated online data.

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Notes

  1. 1.

    http://scikit-learn.org/stable/.

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Authors and Affiliations

  1. School of Computer, Northwestern Polytechnical University, Xiàn, Shaanxi, 710072, China

    Yongjun Li, Zhen Zhang & You Peng

Authors
  1. Yongjun Li
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  2. Zhen Zhang
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  3. You Peng
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Corresponding author

Correspondence to Yongjun Li .

Editor information

Editors and Affiliations

  1. Nanyang Technological University, Singapore, Singapore

    Gao Cong

  2. National Chiao Tung University, Hsinchu, Taiwan

    Wen-Chih Peng

  3. Macquarie University, Sydney, New South Wales, Australia

    Wei Emma Zhang

  4. Wuhan University, Wuhan, China

    Chengliang Li

  5. Nanyang Technological University, Singapore, Singapore

    Aixin Sun

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Li, Y., Zhang, Z., Peng, Y. (2017). A Solution to Tweet-Based User Identification Across Online Social Networks. In: Cong, G., Peng, WC., Zhang, W., Li, C., Sun, A. (eds) Advanced Data Mining and Applications. ADMA 2017. Lecture Notes in Computer Science(), vol 10604. Springer, Cham. https://doi.org/10.1007/978-3-319-69179-4_18

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  • DOI: https://doi.org/10.1007/978-3-319-69179-4_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69178-7

  • Online ISBN: 978-3-319-69179-4

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