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Enhancing stance detection through sequential weighted multi-task learning

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Abstract

The exponential growth of user-generated content on social media platforms, online news outlets, and digital communication has necessitated the development of automated tools for analyzing opinions and attitudes expressed in text. Stance detection, a critical task in Natural Language Processing, aims to identify the underlying perspective or viewpoint of an individual or group toward a specific topic or target. This paper explores the challenges of stance detection, particularly in the context of social media, where brevity, informality, and limited contextual information prevail. While sentiment analysis focuses on explicit sentiment polarity, stance detection classifies the stance or viewpoint of a text toward a target, often of an abstract nature. Motivated by recent achievements in Multi-Task Learning (MTL), this paper addresses the identified gap in the field, advocating further exploration in developing a joint neural architecture that integrates different opinion dimensions. In response, this study introduces two MTL models, Parallel Multi-Task Learning (PMTL) and Sequential Multi-Task Learning (SMTL), which incorporate sentiment analysis and sarcasm detection tasks to enhance stance detection performance. We address the complexities of MTL implementation with Transformer-based architectures and present an accessible architecture for this purpose. This study also proposes and evaluates four task weighting techniques, providing empirical evidence for their effectiveness in MTL models. Through comprehensive evaluations on benchmark datasets in both English and Arabic, we demonstrate that our most proficient model, a multi-target sequential MTL model with hierarchical weighting (SMTL-HW), achieves state-of-the-art results. These contributions underscore the potential of MTL in enhancing stance detection and offer valuable insights into the interaction between sentiment, stance, and sarcasm in text analysis.

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Data availability

Datasets used during the current study are included in Alturayeif et al. (2022); Mohammad et al. (2017). These datasets were derived from the following public domain resources: https://huggingface.co/datasets/NoraAlt/Mawqif_Stance-Detection and https://www.saifmohammad.com/WebPages/StanceDataset.htm.

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Notes

  1. https://github.com/NoraAlt/Stance_Detection.git.

  2. For clarity, we use the term “MTL objective" to refer to the final learning objective of a model, while “loss" represents an individual component within this objective function.

References

  • Al-Ghadir AI, Azmi AM, Hussain A (2021) A novel approach to stance detection in social media tweets by fusing ranked lists and sentiments. Inf Fusion 67:29–40. https://doi.org/10.1016/j.inffus.2020.10.003

    Article  Google Scholar 

  • Aldayel A, Magdy W (2019a) Assessing sentiment of the expressed stance on social media. In: International conference on social informatics, pp 277–286. https://doi.org/10.1007/978-3-030-34971-4_19

  • Aldayel A, Magdy W (2019b) Your stance is exposed! analyzing possible factors forstance detection on social media. Proc ACM on Hum Comput Interact 3:1–20

    Article  Google Scholar 

  • AlDayel A, Magdy W (2021) Stance detection on social media: state of the art and trends. Inf Process Manag. https://doi.org/10.1016/j.ipm.2021.102597

    Article  Google Scholar 

  • Alec R, Jeffrey W, Rewon C, et al (2019) Language models are unsupervised multitask learners. OpenAI Blog 1

  • Alturayeif N, Luqman H, Ahmed M (2022) Mawqif: a multi-label Arabic dataset for target-specific stance detection. In: Proceedings of the the seventh arabic natural language processing workshop (WANLP). Association for Computational Linguistics, Abu Dhabi, United Arab Emirates (Hybrid), pp 174–184. https://doi.org/10.18653/v1/2022.wanlp-1.16, https://aclanthology.org/2022.wanlp-1.16

  • Alturayeif N, Luqman H, Ahmed M (2023) A systematic review of machine learning techniques for stance detection and its applications. Neural Comput Appl 35(7):5113–5144

    Article  Google Scholar 

  • Antoun W, Baly F, Hajj H (2020) Arabert: Transformer-based model for Arabic language understanding. LREC 2020 workshop language resources and evaluation conference

  • Bahuleyan H, Vechtomova O (2017) Uwaterloo at semeval-2017 task 8: Detecting stance toward rumors with topic independent features. In: Proceedings of the 11th international workshop on semantic evaluations (SemEval-2017), pp 461–464

  • Bhatt G, Sharma A, Sharma S et al (2018) Combining neural, statistical and external features for fake news stance identification. Companion Proc Web Conf 2018:1353–1357. https://doi.org/10.1145/3184558.3191577

    Article  Google Scholar 

  • Borges L, Martins B, Calado P (2019) Combining similarity features and deep representation learning for stance detection in the context of checking fake news. J Data Inf Quality (JDIQ) 11:1–26. https://doi.org/10.1145/3287763

    Article  Google Scholar 

  • Chai H, Tang S, Cui J, et al (2022) Improving multi-task stance detection with multi-task interaction network. In: Empirical methods in natural language processing, pp 2990–3000

  • Chauhan DS, Kumar R, Ekbal A (2019) Attention based shared representation for multi-task stance detection and sentiment analysis. In: Neural information processing: 26th international conference, ICONIP 2019, Sydney, NSW, Australia, December 12–15, 2019, proceedings, part V 26. Springer, pp 661–669

  • Chen P, Ye K, Cui X (2021) Integrating n-gram features into pre-trained model: a novel ensemble model for multi-target stance detection. In: Springer Science and Business Media, Deutschland GmbH, international conference on artificial neural networks, pp 269–279. https://doi.org/10.1007/978-3-030-86365-4_22

  • Clark K, Luong MT, Le QV, et al (2020) Electra: pre-training text encoders as discriminators rather than generators

  • Cortis K, Davis B (2021) Over a decade of social opinion mining: a systematic review. Artif Intell Rev 54:4873–4965. https://doi.org/10.1007/s10462-021-10030-2

    Article  Google Scholar 

  • Devlin J, Chang MW, Lee K, et al (2019) Bert: pre-training of deep bidirectional transformers for language understanding

  • Dey K, Shrivastava R, Kaushik S (2017) Twitter stance detection-a subjectivity and sentiment polarity inspired two-phase approach. In: IEEE international conference on data mining workshops (ICDMW), pp 365–372. http://www.noslang.com/dictionary

  • Ebrahimi J, Dou D, Lowd D (2016) A joint sentiment-target-stance model for stance classification in tweets. In: Proceedings of COLING 2016, the 26th international conference on computational linguistics, pp 2656–2665

  • Fang W, Nadeem M, Mohtarami M, et al (2019) Neural multi-task learning for stance prediction. In: Proceedings of the second workshop on fact extraction and verification (FEVER), pp 13–19. https://data.quora.com/

  • Fu Y, Li X, Li Y et al (2022) Incorporate opinion-toward for stance detection. Knowl-Based Syst 246:1–11. https://doi.org/10.1016/j.knosys.2022.108657

    Article  Google Scholar 

  • Ghosh S, Singhania P, Singh S, et al (2019) Stance detection in web and social media: a comparative study. In: International conference of the cross-language evaluation forum for European languages, pp 75–87. https://doi.org/10.1007/978-3-030-28577-7_4

  • Gómez-Suta M, Echeverry-Correa J, Soto-Mejía JA (2023) Stance detection in tweets: a topic modeling approach supporting explainability. Expert Syst Appl 214(119):046

    Google Scholar 

  • Hacohen-Kerner Y, Ido Z, Ya’akobov R (2017) Stance classification of tweets using skip char ngrams. In: Joint European conference on machine learning and knowledge discovery in databases, pp 266–278

  • Hanselowski A, Schiller PVSAB, et al (2018) A retrospective analysis of the fake news challenge stance-detection task. In: Proceedings of the 27th international conference on computational linguistics (COLING 2018)

  • Hardalov M, Arora A, Nakov P, et al (2021) Cross-domain label-adaptive stance detection. In: Proceedings of the 2021 conference on empirical methods in natural language processing, pp 9011–9028

  • Hosseinia M, Dragut E, Mukherjee A (2020) Stance prediction for contemporary issues: data and experiments. In: Proceedings of the eighth international workshop on natural language processing for social media. https://doi.org/10.18653/v1/P17

  • Islam MR, Muthiah S, Ramakrishnan N (2019) Rumorsleuth: joint detection of rumor veracity and user stance. In: 2019 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), pp 131–136. https://doi.org/10.1145/3341161.3342916

  • Kendall A, Gal Y, Cipolla R (2018) Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7482–7491

  • Khandelwal A (2021) Fine-tune longformer for jointly predicting rumor stance and veracity. In: 3rd ACM India joint international conference on data science and management of data, CODS-COMAD 2021, pp 10–19. https://doi.org/10.1145/3430984.3431007

  • Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. In: 3rd International conference on learning representations, ICLR 2015-conference track proceedings

  • Kirkpatrick J, Pascanu R, Rabinowitz N et al (2017) Overcoming catastrophic forgetting in neural networks. Proc Natl Acad Sci USA. https://doi.org/10.1073/pnas.1611835114

    Article  MathSciNet  Google Scholar 

  • Küçük D, Fazli CA (2020) Stance detection: a survey. ACM Comput Surv. https://doi.org/10.1145/3369026

    Article  Google Scholar 

  • Lai M, Cignarella AT, Irazú D, et al (2017) itacos at ibereval2017: detecting stance in catalan and spanish tweets. In: Proceedings of the second workshop on evaluation of human language technologies for Iberian languages (IberEval 2017), pp 185–192

  • Lai M, Cignarella AT, Farías DIH et al (2020) Multilingual stance detection in social media political debates. Comput Speech Lang 63:1–27. https://doi.org/10.1016/j.csl.2020.101075

    Article  Google Scholar 

  • Li Y, Caragea C (2019) Multi-task stance detection with sentiment and stance lexicons. In: Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, pp 6299–6305

  • Li Y, Tian X, Liu T, et al (2015) Multi-task model and feature joint learning, pp 3643–3649

  • Li W, Xu Y, Wang G (2019) Stance detection of microblog text based on two-channel cnn-gru fusion network. IEEE Access 7:145944–145952. https://doi.org/10.1109/ACCESS.2019.2944136

    Article  Google Scholar 

  • Liu X, He P, Chen W, et al (2019a) Multi-task deep neural networks for natural language understanding. In: Proceedings of the 57th annual meeting of the association for computational linguistics, pp 4487–4496

  • Liu Y, Ott M, Goyal N, et al (2019b) Roberta: a robustly optimized bert pretraining approach. arXiv:1907.11692

  • Liu Y, Zhang X, Wegsman D, et al (2022) Politics: pretraining with same-story article comparison for ideology prediction and stance detection

  • Loshchilov I, Hutter F (2017) Decoupled weight decay regularization. arXiv:1711.05101

  • Lukasik M, Bontcheva K, Cohn T et al (2019) Gaussian processes for rumor stance classification in social media. ACM Trans Inf Syst 37:1–24. https://doi.org/10.1145/3295823

    Article  Google Scholar 

  • Ma J, Gao W, Wong KF (2018) Detect rumor and stance jointly by neural multi-task learning. Companion Proc Web Conf 2018:585–593. https://doi.org/10.1145/3184558.3188729

    Article  Google Scholar 

  • Mahabadi RK, Ruder S, Dehghani M, et al (2021) Parameter-efficient multi-task fine-tuning for transformers via shared hypernetworks. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (volume 1: long papers), pp 565–576

  • Mao Y, Wang Z, Liu W, et al (2021) Banditmtl: bandit-based multi-task learning for text classification. In: Proceedings of the 59th annual meeting of the association for computational linguistics and the 11th international joint conference on natural language processing (volume 1: long papers), pp 5506–5516

  • Mao Y, Wang Z, Liu W et al (2022) Metaweighting: learning to weight tasks in multi-task learning. Find Assoc Comput Linguist ACL 2022:3436–3448

    Article  Google Scholar 

  • Mohammad SM, Kiritchenko S, Sobhani P, et al (2016) Semeval-2016 task 6: detecting stance in tweets. In: 10th International workshop on semantic evaluation (SemEval-2016), pp 31–41. https://doi.org/10.18653/v1/s16-1003

  • Mohammad SM, Sobhani P, Kiritchenko S (2017) Stance and sentiment in tweets. ACM Trans Internet Technol. https://doi.org/10.1145/3003433

    Article  Google Scholar 

  • Mohtarami M, Glass J, Nakov P (2019) Contrastive language adaptation for cross-lingual stance detection. In: 2019 Conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP), pp 4442–4452. arXiv:1910.02076

  • Niehues J, Cho E (2017) Exploiting linguistic resources for neural machine translation using multi-task learning. In: WMT 2017—2nd conference on machine translation, proceedings. https://doi.org/10.18653/v1/w17-4708

  • Poddar L, Hsu W, Lee ML, et al (2018) Predicting stances in twitter conversations for detecting veracity of rumors: A neural approach. In: 2018 IEEE 30th international conference on tools with artificial intelligence (ICTAI). IEEE, pp 65–72

  • Raffel C, Shazeer N, Roberts A et al (2020) Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res 21(1):5485–5551

    MathSciNet  Google Scholar 

  • Ribeiro MT, Singh S, Guestrin C (2016) "why should I trust you?": explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, CA, USA, August 13–17, 2016, pp 1135–1144

  • Roy A, Fafalios P, Ekbal A et al (2021) Exploiting stance hierarchies for cost-sensitive stance detection of web documents. J Intell Inf Syst. https://doi.org/10.1007/s10844-021-00642-z

    Article  Google Scholar 

  • Ruder S (2017) An overview of multi-task learning in deep neural networks. arXiv:1706.05098

  • Ruder S, Peters M, Swayamdipta S, et al (2019) Transfer learning in natural language processing tutorial. In: NAACL HLT 2019–2019 conference of the north american chapter of the association for computational linguistics: human language technologies-tutorial abstracts

  • Siddiqua UA, Chy AN, Aono M (2019) Tweet stance detection using multi-kernel convolution and attentive lstm variants. IEICE Trans Inf Syst 102:2493–2503. https://doi.org/10.1587/transinf.2019EDP7080

    Article  Google Scholar 

  • Sobhani P, Mohammad SM, Kiritchenko S (2016) Detecting stance in tweets and analyzing its interaction with sentiment. In: Proceedings of the fifth joint conference on lexical and computational semantics (SEM 2016), pp 159–169

  • Sobhani P, Inkpen D, Zhu X (2017) A dataset for multi-target stance detection, pp 551–557. https://doi.org/10.18653/v1/e17-2088

  • Sobhani P, Inkpen D, Zhu X (2019) Exploring deep neural networks for multitarget stance detection. Comput Intell 35:82–97. https://doi.org/10.1111/coin.12189

    Article  MathSciNet  Google Scholar 

  • Song W, Song Z, Liu L, et al (2020) Hierarchical multi-task learning for organization evaluation of argumentative student essays. In: IJCAI, pp 3875–3881

  • Sun L, Li X, Zhang B, et al (2019a) Learning stance classification with recurrent neural capsule network. In: CCF international conference on natural language processing and Chinese computing, pp 277–289

  • Sun Q, Wang Z, Li S et al (2019b) Stance detection via sentiment information and neural network model. Front Comput Sci 13:127–138. https://doi.org/10.1007/s11704-018-7150-9

    Article  Google Scholar 

  • Sun Q, Xi X, Sun J et al (2022) Stance detection with a multi-target adversarial attention network. ACM Trans Asian Low Resour Lang Inf Process. https://doi.org/10.1145/3544490

    Article  Google Scholar 

  • Upadhyaya A, Fisichella M, Nejdl W (2023a) A multi-task model for sentiment aided stance detection of climate change tweets. In: Proceedings of the international AAAI conference on web and social media, pp 854–865

  • Upadhyaya A, Fisichella M, Nejdl W (2023b) A multi-task model for sentiment aided stance detection of climate change tweets. In: Proceedings of the international AAAI conference on web and social media, pp 854–865

  • Vamvas J, Sennrich R (2020) X-stance: a multilingual multi-target dataset for stance detection. In: 5th SwissText and 16th KONVENS joint conference 2020. arXiv:2003.08385

  • Wang R, Zhou D, Jiang M et al (2019) A survey on opinion mining: from stance to product aspect. IEEE Access 7:41101–41124. https://doi.org/10.1109/ACCESS.2019.2906754

    Article  Google Scholar 

  • Wang H, Wang Y, Song X et al (2023) Quantifying controversy from stance, sentiment, offensiveness and sarcasm: a fine-grained controversy intensity measurement framework on a Chinese dataset. World Wide Web 26(5):3607–3632

    Article  Google Scholar 

  • Wei P, Lin J, Mao W (2018) Multi-target stance detection via a dynamic memory-augmented network. In: The 41st international ACM SIGIR conference on research and development in information retrieval, pp 1229–1232. https://doi.org/10.1145/3209978.3210145

  • Wei P, Xu N, Mao W (2019) Modeling conversation structure and temporal dynamics for jointly predicting rumor stance and veracity. 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), pp 4787–4798. arXiv:1909.08211

  • Wu Y, Schuster M, Chen Z, et al (2016) Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv:1609.08144

  • Yang M, Chen L, Chen X, et al (2019) Knowledge-enhanced hierarchical attention for community question answering with multi-task and adaptive learning. In: IJCAI, pp 5349–5355

  • Ye K, Piao Y, Zhao K, et al (2021) Graph enhanced bert for stance-aware rumor verification on social media. In: International conference on artificial neural networks. Springer, pp 422–435

  • Zhang H, Qian S, Fang Q et al (2021) Multi-modal meta multi-task learning for social media rumor detection. IEEE Trans Multimedia. https://doi.org/10.1109/TMM.2021.3065498

    Article  Google Scholar 

  • Zhang Y, Yang Q (2021) A survey on multi-task learning. In: IEEE transactions on knowledge and data engineering, pp 1–20. arXiv:1707.08114

  • Zhang Y, Ma D, Tiwari P et al (2023) Stance-level sarcasm detection with bert and stance-centered graph attention networks. ACM Trans Internet Technol 23(2):1–21

    Article  Google Scholar 

  • Zhu L, He Y, Zhou D (2020) Neural opinion dynamics model for the prediction of user-level stance dynamics. Inf Process Manag 57:1–13. https://doi.org/10.1016/j.ipm.2019.03.010

    Article  Google Scholar 

  • Zubiaga A, Kochkina E, Liakata M et al (2018) Discourse-aware rumor stance classification in social media using sequential classifiers. Inf Process Manag 54:273–290. https://doi.org/10.1016/j.ipm.2017.11.009

    Article  Google Scholar 

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Acknowledgements

The authors would like to acknowledge the support received from the Saudi Data and AI Authority (SDAIA) and King Fahd University of Petroleum and Minerals (KFUPM) under the SDAIA-KFUPM Joint Research Center for Artificial Intelligence Grant JRC-AI-RFP-14.

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Authors and Affiliations

  1. Information and Computer Science Department, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

    Nora Alturayeif, Hamzah Luqman & Moataz Ahmed

  2. Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia

    Nora Alturayeif

  3. SDAIA-KFUPM Joint Research Center for Artificial Intelligence, KFUPM, Dhahran, Saudi Arabia

    Hamzah Luqman

  4. Interdisciplinary Research Center of Intelligent Secure Systems (IRC-ISS), KFUPM, Dhahran, Saudi Arabia

    Moataz Ahmed

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  1. Nora Alturayeif
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All authors conceptualized the work and research objectives. N.A. designed and implemented the experiments under the supervision of H.L. and M.A. N.A. wrote the original manuscript text. All authors revised and reviewed the final manuscript.

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Correspondence to Nora Alturayeif.

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Alturayeif, N., Luqman, H. & Ahmed, M. Enhancing stance detection through sequential weighted multi-task learning. Soc. Netw. Anal. Min. 14, 7 (2024). https://doi.org/10.1007/s13278-023-01169-7

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