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A Real-Time Twitter Trend Analysis and Visualization Framework

Author

Listed:
  • Jamuna S. Murthy

    (PES University, Bengaluru, India)

  • Siddesh G.M.

    (Ramaiah Institute of Technology, Bengaluru, India)

  • Srinivasa K.G.

    (National Institute of Technical Teachers' Training & Research, Chandigarh, India)

Abstract

Trend analysis over Twitter offers organizations a fast and effective way of predicting the future trends. In the recent years, a wide range of indicators and methods were used for predicting the trend on Twitter with varying results, unfortunately most of the research focused only on the emerging trends which has gained long-term attention on the Twitter platform. This article depicts trend variations, i.e. to predict whether the trend on Twitter will gain attention or not in the next few hours. Hence a novel method called: “Twitter Trend Momentum (TTM)” is introduced for trend prediction which is the enhancement of a well-known stock market indicator called moving average convergence divergence (MACD). Reason analysis for trend variation is also carried out as an extension to the authors' research work. An evaluation of the framework showed the best results which are applied to build a real-time web application called “TwitTrend.” The application acts as a real-time update and recommendation system of top trends to users.

Suggested Citation

  • Jamuna S. Murthy & Siddesh G.M. & Srinivasa K.G., 2019. "A Real-Time Twitter Trend Analysis and Visualization Framework," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 15(2), pages 1-21, April.
    • Handle: RePEc:igg:jswis0:v:15:y:2019:i:2:p:1-21
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    Cited by:

    1. Akilandeswari J. & Jothi G. & Dhanasekaran K. & Kousalya K. & Sathiyamoorthi V., 2022. "Hybrid Firefly-Ontology-Based Clustering Algorithm for Analyzing Tweets to Extract Causal Factors," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 18(1), pages 1-27, January.

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