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Early Spam Detection Using Time-Based Cache in Graph database

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Abstract

Social networking is prevalent everywhere. Technology has made the world seem smaller by bringing individuals together, reconnecting people, and providing other opportunities for connection. However, the risk of data fraud has also increased. The early detection of fraud needs a deep analysis of tweets, which has been done using Neo4J. The Twitter API receives requests from thousands of developers every single day. Limits are imposed on the number of requests that may be made to assist in managing the overwhelming amount of these requests. The most typical request limit period is 15 min. If an endpoint has a rate limit of 1000 requests/15 min, then 1000 requests are permitted over 15 min. Every time a real-time tweet analysis error is returned to the application when these limits are exceeded, online updation of the Neo4J database is difficult. In the proposed method, first and foremost, a new time-based cache (TmCache) is introduced between the database and Twitter API, which removes the complexities of Twitter API and reduces the analyzing time by 85.36%. Furthermore, a machine learning-based approach Multinomial Naive Bayes classifier is used to classify the spam and non-spam tweets with the lowest training time and 96.6% accuracy. The proposed model is helpful in the early detection of spam accurately and can be used in reducing the risk of propagation of agendas which are a major threat to society at large in the current era of social networking.

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Funding

The authors would like to thank the Technical Education Quality Improvement Programme (TEQIP-III), for supporting the research.

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Authors and Affiliations

  1. Motilal Nehru National Institute of Technology, Prayagraj, Uttar Pradesh, India

    Sakshi Srivastava, Supriya Agrahari & Anil Kumar Singh

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  1. Sakshi Srivastava
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  2. Supriya Agrahari
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  3. Anil Kumar Singh
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Contributions

Sakshi Srivastava MNNIT Allahabad (a) participated in analysis and interpretation of the data, (b) drafted the article or revised it critically for important intellectual content, and (c) gave approval to the final version. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue. Anil Kumar Singh MNNIT Allahabad (a) drafted the article or revised it critically for important intellectual content and (b) gave approval to the final version. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue.

Corresponding author

Correspondence to Sakshi Srivastava.

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On behalf of all the authors, the corresponding author states that there is no conflict of interest.

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Hereby, I, Sakshi Srivastava, consciously assure that for the Big Data Analysis using Time-Based Cache, the following is fulfilled: (1) this material is the authors’ own original work, which has not been previously published elsewhere. (2) The paper is not currently being considered for publication elsewhere.

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Srivastava, S., Agrahari, S. & Singh, A.K. Early Spam Detection Using Time-Based Cache in Graph database. New Gener. Comput. 41, 607–634 (2023). https://doi.org/10.1007/s00354-023-00223-4

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