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Quantifying controversy from stance, sentiment, offensiveness and sarcasm: a fine-grained controversy intensity measurement framework on a Chinese dataset

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Abstract

Controversy measurement on social media plays an important part in understanding public opinion. Various topics are frequently hotly debated on social media platforms including Twitter and Sina Weibo. People sometimes use offensive or sarcastic language to convey their opinions about a topic or a source post, which might spark heated discussions and controversy towards related topics. Recent researches take controversy detection as a binary classification problem with two labels: controversy or non-controversy. The reason might be lacking a comprehensive understanding of why the controversy happened and a specific imagination of how it will be used in the downstream tasks. However, we believe that the degree of controversy courted by posts or topics in a real scenario varied. And fine-grained measurement of controversy will be beneficial to public sentiment identification, influence assessment and other social network analysis tasks. We also notice that the existing benchmarks of controversy detection are not applicable for fine-grained topic-level controversy measurement. In this paper, we present ProsCons, a large-scale comprehensive Chinese dataset that includes 245 topics and 32,667 posts with pro, con or neutral stances. Based on that, we design a controversy measurement framework for measuring the controversy intensity that topics sparked. This framework considers the degree of antagonism in terms of stance and sentiment, as well as the irrational degree (offensive or sarcasm) of a post to compute a controversy intensity. ProsCons provides a new benchmark for Chinese stance detection, offensive language and sarcasm detection, contributing to the multi-task learning of them. We conduct extensive experiments on ProsCons and provide baselines for these tasks. The experimental results highlight the challenges of the aforementioned tasks based on the ProsCons.

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Availability of data and materials

Please request the ProsCons dataset at wanghaiyang19@nudt.edu.cn. X-stance can be accessed in http://doi.org/10.5281/zenodo.3831317. COVID-19-Stance can be accessed in https://github.com/kglandt/stance-detection-in-covid-19-tweets.

Notes

  1. Please request the ProsCons dataset at wanghaiyang19@nudt.edu.cn.

  2. https://github.com/hankcs/pyhanlp.

  3. https://ai.baidu.com/easydata/

  4. The analysis in this section is limited and the heuristic inference based on the ProsCons dataset only.

  5. https://huggingface.co/Helsinki-NLP.

  6. https://github.com/pysentimiento/pysentimiento.

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Funding

This work was supported by the National Natural Science Foundation of China (62172428).

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  1. National University of Defense Technology, 109 Deya Road, Changsha, 410003, Hunan, China

    Haiyang Wang, Ye Wang, Xin Song, Bin Zhou, Xuechen Zhao & Feng Xie

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  1. Haiyang Wang
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  3. Xin Song
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  4. Bin Zhou
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  5. Xuechen Zhao
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  6. Feng Xie
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Contributions

Bin Zhou raised the need of this work and provided funding. Haiyang Wang was responsible for data collection, annotation, experiment and completed the initial version of the manuscript. Ye Wang revised the manuscript and enriched the experiments. Xin Song, Xuechen Zhao and Xie Feng analysed the data and experiment results.

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Correspondence to Ye Wang.

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Wang, H., Wang, Y., Song, X. et al. Quantifying controversy from stance, sentiment, offensiveness and sarcasm: a fine-grained controversy intensity measurement framework on a Chinese dataset. World Wide Web 26, 3607–3632 (2023). https://doi.org/10.1007/s11280-023-01191-x

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  • DOI: https://doi.org/10.1007/s11280-023-01191-x

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