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Analyzing the uncharted territory of monetizing scam Videos on YouTube

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Abstract

In recent times, the Indian government has launched campaigns cautioning users against malicious apps which trick users into expending money on the app in the false hopes of quick passive income. They are also tricked into sharing their personally identifiable information in many cases. It is observed that such apps are heavily promoted on video sharing sites such as YouTube which results into exploitative monetization of user’s watch time and degrades user experience. In this work, we perform an investigative study to analyze and identify such videos termed as Monetary Scam videos. A detailed analysis of the characteristics of monetary scam videos has been performed based on contextual and statistical features. The context of a video targeted for Indian audience contains non-standard transliteration of Hindi words written in Roman script which makes existing video context-based models unsuitable for identification of such monetary scam videos. Thus, it is required to build a solution specific to the Indian context. A total of 1500 videos were collected and labeled for this work. Two types of features: 1) textual attributes and 2) Metadata-based statistical features have been used for three-class and two-class classification of the collected videos using five machine learning classifiers. In the experimental results, the Random Forest classifier predicts scam videos with the best accuracy scores among all the five classifiers in both three-class and two-class classifiers. A comparative analysis with three state-of-the-art models from similar studies depict that our model outperforms others for our collected dataset in both three-class and two-class classifications.

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Data Availability

Public data has been collected from YouTube videos through YouTube Data API. It is not available for sharing without the permission of YouTube. Only the video IDs and ground-truth labels can be asked for sharing by mailing the authors.

Notes

  1. https://english.alaraby.co.uk/english/news/2020/6/2/saudi-bots-spam-fake-news-tweets-against-qatar.

  2. https://gadgets.ndtv.com/apps/news/wolfrat-whatsapp-facebook-messenger-line-wolf-research-2232314.

  3. https://www.ft.com/content/fbdf0f0d-c3a6-42dd-97b0-7f4b4ee63170.

  4. https://www.statista.com/statistics/1132981/number-removed-youtube-videos-worldwide-by-views/.

  5. https://www.statista.com/topics/2019/youtube/.

  6. https://edition.cnn.com/2019/12/13/tech/youtube-fake-accounts-viral/index.html.

  7. https://www.thetimes.co.uk/article/youtube-cashes-in-on-neo-nazis-hate-videos-9gg0nbvd6.

  8. https://techcrunch.com/2013/08/27/google-dumps-video-responses-from-youtube-due-to-dismal-0004-click-through-rate/.

  9. https://developers.google.com/youtube/v3/.

  10. https://trends.google.com/trends/?geo=IN.

  11. https://pythonspot.com/nltk-stop-words/.

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Author notes

  1. Ashutosh Tripathi, Mohona Ghosh and Kusum Bharti: These authors contributed equally to this work.

Authors and Affiliations

  1. Computer Science and Engineering, PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur, India, Jabalpur, 482005, MP, India

    Ashutosh Tripathi & Kusum Bharti

  2. Information Technology, Indira Gandhi Delhi Technical University for Women, Delhi, 110006, Delhi, India

    Mohona Ghosh

Authors
  1. Ashutosh Tripathi
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  2. Mohona Ghosh
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  3. Kusum Bharti
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Correspondence to Ashutosh Tripathi, Mohona Ghosh or Kusum Bharti.

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Tripathi, A., Ghosh, M. & Bharti, K. Analyzing the uncharted territory of monetizing scam Videos on YouTube. Soc. Netw. Anal. Min. 12, 119 (2022). https://doi.org/10.1007/s13278-022-00945-1

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