Automatic Twitter Stance Detection on Politically Controversial Issues: A Study on Covid-19’s CPI
Resumo
Prever o posicionamento de usuários de mídias sociais sobre um tópico tema pode ser desafiador, especialmente para casos não supervisionados. Neste trabalho foram utilizadas postagens retuitadas como elementos de interação dos usuários, para calcular as semelhanças entre os mais ativos dentro de uma discussão. A detecção de posicionamento para esses usuários foi realizada usando técnicas de redução de dimensionalidade e clusterização, modelagem de tópicos usando embeddings contextualizados, e rotulação automática de clusters baseada em termos recorrentes em cada grupo. Esta abordagem produziu um pequeno número de clusters de usuários (entre 2 e 3), com uniformidade na rotulação dos usuários em diferentes bases superior a 98%.
Referências
Bechini, A., Ducange, P., Marcelloni, F., and Renda, A. (2020). Stance analysis of twitter users: the case of the vaccination topic in italy. IEEE Intelligent Systems.
D’Andrea, E., Ducange, P., Bechini, A., Renda, A., and Marcelloni, F. (2019). Monitoring the public opinion about the vaccination topic from tweets analysis. Expert Systems with Applications, 116:209–226.
Darwish, K., Stefanov, P., Aupetit, M., and Nakov, P. (2020). Unsupervised user stance detection on twitter. In Proceedings of the International AAAI Conference on Web and Social Media, volume 14, pages 141–152.
Ebeling, R., Sáenz, C. A. C., Nobre, J., and Becker, K. (2020). Quarenteners vs. chloroquiners: A framework to analyze how political polarization affects the behavior of groups. In 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), pages 203–210. IEEE.
Ebeling, R., Sáenz, C. A. C., Nobre, J., and Becker, K. (2021). The effect of political polarization on social distance stances in the brazilian covid-19 scenario. Journal of Information and Data Management, 12(1).
Gonçalves, R., Oliveira, T., Toth, J., and Anchieta, W. (2021). Cpi do negacionismo: A ciência no campo das disputas político-ideológicas. [link], Acesso em 14-08-2021.
Grootendorst, M. (2020). Bertopic: leveraging bert and c-tf-idf to create easily interpretable topics. URL https://doi.org/10.5281/zenodo, 4381785.
Küçük, D. and Can, F. (2020). Stance detection: A survey. ACM Computing Surveys (CSUR), 53(1):1–37.
Lin, J., Mao,W., and Zhang, Y. (2017). An enhanced topic modeling approach to multiple stance identification. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 2167–2170.
Magdy,W., Darwish, K., and Weber, I. (2016). # failedrevolutions: Using twitter to study the antecedents of isis support. In 2016 AAAI Spring Symposium Series.
McInnes, L., Healy, J., Saul, N., and Großberger, L. (2018). Umap: Uniform manifold approximation and projection. Journal of Open Source Software, 3(29).
Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X., and Cherry, C. (2016). Semeval-2016 task 6: Detecting stance in tweets. In Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), pages 31–41.
Mohammad, S. M., Sobhani, P., and Kiritchenko, S. (2017). Stance and sentiment in tweets. ACM Transactions on Internet Technology (TOIT), 17(3):1–23.
Penteado, C. L. C., Campos-Domínguez, E., Santos, P. D., Goya, D. H., Núñez, M. M., and Lázaro, M. M. (2021). Discourse and disinformation on covid-19 vaccination in spain and brazil: a case study on the twitter debate. In Palau-Sampio, editor, Contemporary Politics, Communication, and the Impact on Democracy, chapter in press. IGI Global, Hershey, PA.
Popat, K., Mukherjee, S., Yates, A., and Weikum, G. (2019). Stancy: Stance classification based on consistency cues. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6413–6418.
Rashed, A., Kutlu, M., Darwish, K., Elsayed, T., and Bayrak, C. (2021). Embeddingsbased clustering for target specific stances: The case of a polarized turkey. In Proceedings of the International AAAI Conference on Web and Social Media, volume 15, pages 537–548.
Samih, Y. and Darwish, K. (2021). A few topical tweets are enough for effective user stance detection. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2637–2646.
Sirrianni, J. W., Liu, X., and Adams, D. (2021). Predicting stance polarity and intensity in cyber argumentation with deep bidirectional transformers. IEEE Transactions on Computational Social Systems, 8(3):655–667.
Souza, F., Nogueira, R., and Lotufo, R. (2020). BERTimbau: pretrained BERT models for Brazilian Portuguese. In 9th Brazilian Conference on Intelligent Systems, BRACIS, Rio Grande do Sul, Brazil, October 20-23 (to appear).
Vamvas, J. and Sennrich, R. (2020). X-stance: A multilingual multi-target dataset for stance detection. In 5th SwissText & 16th KONVENS Joint Conference 2020, page 9. CEUR-WS. org.
Vychegzhanin, S. V. and Kotelnikov, E. V. (2019). Stance detection based on ensembles of classifiers. Programming and Computer Software, 45(5):228–240.
Wagner Filho, J. A., Wilkens, R., Idiart, M., and Villavicencio, A. (2018). The brwac corpus: A new open resource for brazilian portuguese. In Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018).
Wojatzki, M. and Zesch, T. (2016). ltl. uni-due at semeval-2016 task 6: Stance detection in social media using stacked classifiers. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 428–433.
Zarrella, G. and Marsh, A. (2016). Mitre at semeval-2016 task 6: Transfer learning for stance detection. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 458–463.
Zeng, L., Starbird, K., and Spiro, E. S. (2016). # unconfirmed: Classifying rumor stance in crisis-related social media messages. In Tenth International AAAI Conference on Web and Social Media.
D’Andrea, E., Ducange, P., Bechini, A., Renda, A., and Marcelloni, F. (2019). Monitoring the public opinion about the vaccination topic from tweets analysis. Expert Systems with Applications, 116:209–226.
Darwish, K., Stefanov, P., Aupetit, M., and Nakov, P. (2020). Unsupervised user stance detection on twitter. In Proceedings of the International AAAI Conference on Web and Social Media, volume 14, pages 141–152.
Ebeling, R., Sáenz, C. A. C., Nobre, J., and Becker, K. (2020). Quarenteners vs. chloroquiners: A framework to analyze how political polarization affects the behavior of groups. In 2020 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), pages 203–210. IEEE.
Ebeling, R., Sáenz, C. A. C., Nobre, J., and Becker, K. (2021). The effect of political polarization on social distance stances in the brazilian covid-19 scenario. Journal of Information and Data Management, 12(1).
Gonçalves, R., Oliveira, T., Toth, J., and Anchieta, W. (2021). Cpi do negacionismo: A ciência no campo das disputas político-ideológicas. [link], Acesso em 14-08-2021.
Grootendorst, M. (2020). Bertopic: leveraging bert and c-tf-idf to create easily interpretable topics. URL https://doi.org/10.5281/zenodo, 4381785.
Küçük, D. and Can, F. (2020). Stance detection: A survey. ACM Computing Surveys (CSUR), 53(1):1–37.
Lin, J., Mao,W., and Zhang, Y. (2017). An enhanced topic modeling approach to multiple stance identification. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pages 2167–2170.
Magdy,W., Darwish, K., and Weber, I. (2016). # failedrevolutions: Using twitter to study the antecedents of isis support. In 2016 AAAI Spring Symposium Series.
McInnes, L., Healy, J., Saul, N., and Großberger, L. (2018). Umap: Uniform manifold approximation and projection. Journal of Open Source Software, 3(29).
Mohammad, S., Kiritchenko, S., Sobhani, P., Zhu, X., and Cherry, C. (2016). Semeval-2016 task 6: Detecting stance in tweets. In Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), pages 31–41.
Mohammad, S. M., Sobhani, P., and Kiritchenko, S. (2017). Stance and sentiment in tweets. ACM Transactions on Internet Technology (TOIT), 17(3):1–23.
Penteado, C. L. C., Campos-Domínguez, E., Santos, P. D., Goya, D. H., Núñez, M. M., and Lázaro, M. M. (2021). Discourse and disinformation on covid-19 vaccination in spain and brazil: a case study on the twitter debate. In Palau-Sampio, editor, Contemporary Politics, Communication, and the Impact on Democracy, chapter in press. IGI Global, Hershey, PA.
Popat, K., Mukherjee, S., Yates, A., and Weikum, G. (2019). Stancy: Stance classification based on consistency cues. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6413–6418.
Rashed, A., Kutlu, M., Darwish, K., Elsayed, T., and Bayrak, C. (2021). Embeddingsbased clustering for target specific stances: The case of a polarized turkey. In Proceedings of the International AAAI Conference on Web and Social Media, volume 15, pages 537–548.
Samih, Y. and Darwish, K. (2021). A few topical tweets are enough for effective user stance detection. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2637–2646.
Sirrianni, J. W., Liu, X., and Adams, D. (2021). Predicting stance polarity and intensity in cyber argumentation with deep bidirectional transformers. IEEE Transactions on Computational Social Systems, 8(3):655–667.
Souza, F., Nogueira, R., and Lotufo, R. (2020). BERTimbau: pretrained BERT models for Brazilian Portuguese. In 9th Brazilian Conference on Intelligent Systems, BRACIS, Rio Grande do Sul, Brazil, October 20-23 (to appear).
Vamvas, J. and Sennrich, R. (2020). X-stance: A multilingual multi-target dataset for stance detection. In 5th SwissText & 16th KONVENS Joint Conference 2020, page 9. CEUR-WS. org.
Vychegzhanin, S. V. and Kotelnikov, E. V. (2019). Stance detection based on ensembles of classifiers. Programming and Computer Software, 45(5):228–240.
Wagner Filho, J. A., Wilkens, R., Idiart, M., and Villavicencio, A. (2018). The brwac corpus: A new open resource for brazilian portuguese. In Proceedings of the eleventh international conference on language resources and evaluation (LREC 2018).
Wojatzki, M. and Zesch, T. (2016). ltl. uni-due at semeval-2016 task 6: Stance detection in social media using stacked classifiers. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 428–433.
Zarrella, G. and Marsh, A. (2016). Mitre at semeval-2016 task 6: Transfer learning for stance detection. In Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), pages 458–463.
Zeng, L., Starbird, K., and Spiro, E. S. (2016). # unconfirmed: Classifying rumor stance in crisis-related social media messages. In Tenth International AAAI Conference on Web and Social Media.
Publicado
29/11/2021
Como Citar
SANTOS, Patricia D.; GOYA, Denise H..
Automatic Twitter Stance Detection on Politically Controversial Issues: A Study on Covid-19’s CPI. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 18. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 524-535.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2021.18281.
