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A novel weighted majority voting-based ensemble approach for detection of road accidents using social media data

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Abstract

Early detection of accidents and rescue are of paramount importance in the reduction of fatalities. Social media data, which has evolved to become an important source of sharing information, plays a great role in building machine learning-based models for classifying posts related to accidents. Since the context of the word “accident” is difficult to determine in a posting, various works in literature have developed better classifiers for predicting whether the posting is actually related to an accident. However, an ensemble of classifiers are known to provide better performance than the basic models. Therefore, in this direction, we present a novel weighted majority voting-based ensemble approach for context classification of tweets (WM-ECCT) to detect whether the tweets are related or unrelated to road accidents. For the proposed ensemble model, the weighting scheme is based on the principle of false prediction to true prediction ratio. Also, the proposed model uses the multi-inducer technique and bootstrap sampling to reduce misclassification rates. Moreover, we propose a context-aware labeling approach for the annotation of tweets into related and unrelated categories. Experiments conducted reveal that the proposed ensemble model outperforms the different standalone machine learning and ensemble models on various performance measures.

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Data availability

On reasonable request from the corresponding author. No datasets were generated or analysed during the current study.

Notes

  1. In this work, basic models and Single-ML classifiers are used interchangeably

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Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology, Warangal, Telangana, 506004, India

    Sanjib Kumar Raul, Rashmi Ranjan Rout & D. V. L. N. Somayajulu

  2. Department of Computer Science and Engineering, Indian Institute of Information Technology Design and Manufacturing (IIITDM), Kurnool, Andhra Pradesh, 518002, India

    D. V. L. N. Somayajulu

Authors
  1. Sanjib Kumar Raul
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  2. Rashmi Ranjan Rout
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  3. D. V. L. N. Somayajulu
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Contributions

Sanjib Kumar Raul: Conception, Design, Proposed Model, Model Analysis, Drafting, Data collection, Experimentation, Review, and Approve. Rashmi Ranjan Rout: Conception, Design, Model Analysis, Drafting, Review, and Approve. D.V.L.N. Somayajulu: Conception, Design, Model Analysis, Drafting, Review, and Approve.

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Correspondence to Sanjib Kumar Raul.

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Raul, S.K., Rout, R.R. & Somayajulu, D.V.L.N. A novel weighted majority voting-based ensemble approach for detection of road accidents using social media data. Soc. Netw. Anal. Min. 14, 214 (2024). https://doi.org/10.1007/s13278-024-01368-w

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