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An online and highly-scalable streaming platform for filtering trolls with transfer learning

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Abstract

The internet has reached a mature stage of development, and Online Social Media (OSM) platforms such as Twitter and Facebook have become vital channels for public communication and discussion on matters of public interest. However, these platforms are often plagued by improper statements or content, propagated by anonymous users and trolls, which negatively impact both the platforms and their users. Existing methods for dealing with inappropriate information rely on (semi)-manual offline assessments, which do not fully account for the streaming nature of OSM feeds. In this paper, we implement a robust and decoupled system that considers social media data as streaming data. With a publisher and consumer model, our system can process more than 179 MB of data per second with only 166.3 ms latency using Apache Kafka. Accordingly, we deploy a well-trained transfer learning model to classify incoming data streams, with an accuracy of 0.836. Our proposed architecture has the potential to assist online communities in developing more constructive and flawless OSM platforms. We believe that our contribution will help address the challenges associated with improper content on OSM platforms and pave the way for the development of more effective and efficient solutions.

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Data Availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Notes

  1. https://www.taipeitimes.com/News/taiwan/archives/2015/04/23/2003616602.

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Funding

This work was partially supported by the National Science and Technology Council (NSTC), Taiwan (R.O.C.), under Grants Number 111-2622-E-029-003-, 111-2811-E-029-001-, 111-2621-M-029- 004-, and 110-2222-E-029-001-

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Authors and Affiliations

  1. Department of Computer Science, Tunghai University, No. 1727, Sec.4, Taiwan Blvd., Taichung, 407224, Taiwan R.O.C.

    Chun-Ming Lai, Ting-Wei Chang & Chao-Tung Yang

  2. Research Center for Smart Sustainable Circular Economy, Tunghai University, No. 1727, Sec.4, Taiwan Blvd., Taichung, 407224, Taiwan R.O.C.

    Chao-Tung Yang

Authors
  1. Chun-Ming Lai
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  2. Ting-Wei Chang
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Contributions

CM Lai conceived research design, structuring the paper and interpreting the findings. TW Chang collected the data, conducted experiment evaluation and wrote a part of the manuscript. CT Yang supervised the research and reviewed the manuscript.

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Correspondence to Chao-Tung Yang.

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Lai, CM., Chang, TW. & Yang, CT. An online and highly-scalable streaming platform for filtering trolls with transfer learning. J Supercomput 79, 16664–16687 (2023). https://doi.org/10.1007/s11227-023-05312-1

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