CoChat
Sign Up
Cited by 74Computational and Text Analysis MethodsSentiment Analysis and Opinion MiningEthics and Social Impacts of AI

Large language models (LLM) in computational social science: prospects, current state, and challenges

Open access
Thapa, Surendrabikram, Shiwakoti, Shuvam, Shah, Siddhant Bikram, Adhikari, Surabhi, Veeramani, Hariram, Nasim, Mehwish, Naseem, Usman·2025·Social Network Analysis and Mining
DOI 10.1007/s13278-025-01428-9
Open this paper in CoChat — free account

Abstract

The advent of large language models (LLMs) has marked a new era in the transformation of computational social science (CSS). This paper dives into the role of LLMs in CSS, particularly exploring their potential to revolutionize data analysis and content generation and contribute to a broader understanding of social phenomena. We begin by discussing the applications of LLMs in various computational problems in social science including sentiment analysis, hate speech detection, stance and humor detection, misinformation detection, event understanding, and social network analysis, illustrating their capacity to generate nuanced insights into human behavior and societal trends. Furthermore, we explore the innovative use of LLMs in generating social media content. We also discuss the various ethical, technical, and legal issues these applications pose, and considerations required for responsible LLM usage. We further present the challenges associated with data bias, privacy, and the integration of these models into existing research frameworks. This paper aims to provide a solid background on the potential of LLMs in CSS, their past applications, current problems, and how they can pave the way for revolutionizing CSS.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/s13278-025-01428-9

Cites

11 of 11 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 141

  1. Aiyappa R, Senthilmani S, An J, Kwak H, Ahn Y-Y (2024) Benchmarking zero-shot stance detection with flant5-xxl: Insights from training data, prompting, and decoding strategies into its near-sota performance. arXiv preprint arXiv:2403.00236 · arXiv
  2. Alam F, Sajjad H, Imran M, Ofli F (2021) CrisisBench: benchmarking crisis-related social media datasets for humanitarian information processing. In: Proceedings of the international AAAI conference on web and social media, vol 15, pp 923–932
  3. Alayrac J-B, Donahue J, Luc P, Miech A, Barr I, Hasson Y, Lenc K, Mensch A, Millican K, Reynolds M et al (2022) Flamingo: a visual language model for few-shot learning. Adv Neural Inf Process Syst 35:23716–23736
  4. Alsaedi N, Burnap P (2015) Feature extraction and analysis for identifying disruptive events from social media. In: Proceedings of the 2015 IEEE/ACM international conference on advances in social networks analysis and mining 2015. pp 1495–1502
  5. Annamoradnejad I, Zoghi G (2020) Colbert: Using BERT sentence embedding for humor detection. arXiv preprint arXiv:2004.12765 1(3) · arXiv
  6. Augenstein I, Rocktäschel T, Vlachos A, Bontcheva K (2016) Stance detection with bidirectional conditional encoding. In: Su J, Duh K, Carreras X (eds.) Proceedings of the 2016 conference on empirical methods in natural language processing. Association for Computational Linguistics, Austin, Texas, pp 876–885. https://doi.org/10.18653/v1/D16-1084 . https://aclanthology.org/D16-1084 · DOI
  7. Bakker F, Van Heusden R, Marx M (2024) Timeline extraction from decision letters using ChatGPT. In: Hürriyetoğlu A, Tanev H, Thapa S, Uludoğan G (eds.) Proceedings of the 7th workshop on challenges and applications of automated extraction of socio-political events from text (CASE 2024). Association for Computational Linguistics, St. Julians, Malta, pp. 24–31. https://aclanthology.org/2024.case-1.3
  8. Barić A, Papay S, Padó S (2024) Actor identification in discourse: a challenge for LLMS? arXiv preprint arXiv:2402.00620 · arXiv
  9. Batra PK, Saxena A, Goel C et al (2020) Election result prediction using twitter sentiments analysis. In: 2020 sixth international conference on parallel, distributed and grid computing (PDGC), IEEE, pp 182–185
  10. Baumann F, Lorenz-Spreen P, Sokolov IM, Starnini M (2020) Modeling echo chambers and polarization dynamics in social networks. Phys Rev Lett 124(4):048301
  11. Bellamkonda S, Lohakare M, Patel S (2022) A dataset for detecting humor in Telugu social media text. In: Chakravarthi BR, Priyadharshini R, Madasamy AK, Krishnamurthy, P, Sherly E, Mahesan S (eds.) Proceedings of the Second Workshop on Speech and Language Technologies for Dravidian Languages. Association for Computational Linguistics, Dublin, Ireland, pp 9–14. https://doi.org/10.18653/v1/2022.dravidianlangtech-1.2 . https://aclanthology.org/2022.dravidianlangtech-1.2 · DOI
  12. Benson E, Haghighi A, Barzilay R (2011) Event discovery in social media feeds. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pp 389–398
  13. Bhandari A, Shah SB, Thapa S, Naseem U, Nasim M (2023) CrisisHateMM: Multimodal analysis of directed and undirected hate speech in text-embedded images from Russia-Ukraine conflict. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 1993–2002
  14. Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A et al (2020) Language models are few-shot learners. Adv Neural Inf Process Syst 33:1877–1901
  15. Bruning PF, Alge BJ, Lin H-C (2020) Social networks and social media: understanding and managing influence vulnerability in a connected society. Bus Horiz 63(6):749–761
  16. Cai Y, Cai H, Wan X (2019) Multi-modal sarcasm detection in Twitter with hierarchical fusion model. In: Korhonen A, Traum D, Màrquez L (eds.) Proceedings of the 57th annual meeting of the association for computational linguistics. Association for Computational Linguistics, Florence, Italy. pp 2506–2515. https://doi.org/10.18653/v1/P19-1239 . https://aclanthology.org/P19-1239 · DOI
  17. Cao Y, Nair AM, Eyimife E, Soofi NJ, Subbalakshmi K, Wullert II, JR, Basu C, Shallcross D (2024) Can large language models detect misinformation in scientific news reporting? arXiv preprint arXiv:2402.14268 · arXiv
  18. Castro S, Hazarika D, Pérez-Rosas V, Zimmermann R, Mihalcea R, Poria S (2019) Towards multimodal sarcasm detection (an _obviously_ perfect paper). arXiv preprint arXiv:1906.01815 · arXiv
  19. Chakravarthi BR, Bharathi B, Mccrae JP, Zarrouk M, Bali K, Buitelaar P (2022) Proceedings of the second workshop on language technology for equality, diversity and inclusion. In: Proceedings of the second workshop on language technology for equality, diversity and inclusion
  20. Chakravarthi BR (2022) Hope speech detection in YouTube comments. Soc Netw Anal Min 12(1):75
  21. Chang S, Wang R, Ren P, Huang H (2024) Enhanced short text modeling: leveraging large language models for topic refinement. arXiv preprint arXiv:2403.17706 · arXiv
  22. Chaudhari A, Parseja A, Patyal A (2020) CNN based hate-o-meter: a hate speech detecting tool. In: 2020 third international conference on smart systems and inventive technology (ICSSIT). IEEE, pp 940–944
  23. Chen R, Qin C, Jiang W, Choi D (2024) Is a large language model a good annotator for event extraction? In: Proceedings of the AAAI conference on artificial intelligence, vol 38, pp 17772–17780
  24. Chiruzzo L, Castro S, Rosá A (2020) HAHA 2019 dataset: a corpus for humor analysis in Spanish. In: Calzolari N, Béchet F, Blache P, Choukri K, Cieri C, Declerck T, Goggi S, Isahara H, Maegaard B, Mariani J, Mazo H, Moreno A, Odijk J, Piperidis S (eds.) Proceedings of the twelfth language resources and evaluation conference. European Language Resources Association, Marseille, France. pp 5106–5112. https://aclanthology.org/2020.lrec-1.628
  25. Choi EC, Ferrara E (2024) Fact-GPT: fact-checking augmentation via claim matching with LLMS. arXiv preprint arXiv:2402.05904 · arXiv
  26. Chowdhury AG, Chadha A (2023) Generative data augmentation using LLMs improves distributional robustness in question answering. arXiv preprint arXiv:2309.06358 · arXiv
  27. Chung HW, Hou L, Longpre S, Zoph B, Tay Y, Fedus W, Li Y, Wang X, Dehghani M, Brahma S et al (2022) Scaling instruction-finetuned language models. arXiv preprint arXiv:2210.11416 · arXiv
  28. Cruickshank IJ, Ng LHX (2023) Use of large language models for stance classification. arXiv preprint arXiv:2309.13734 · arXiv
  29. Darwish K, Stefanov P, Aupetit M, Nakov P (2020) Unsupervised user stance detection on twitter. In: Proceedings of the international AAAI conference on web and social media, vol 14, pp 141–152
  30. Deng X, Bashlovkina V, Han F, Baumgartner S, Bendersky M (2023) LLMS to the moon? reddit market sentiment analysis with large language models. In: Companion proceedings of the ACM web conference 2023, pp 1014–1019
  31. Devarajan GG, Nagarajan SM, Amanullah SI, Mary SSA, Bashir AK (2023) AI-assisted deep NLP-based approach for prediction of fake news from social media users. IEEE Trans Comput Soc Syst
  32. Dey K, Shrivastava R, Kaushik S (2018) Topical stance detection for twitter: Aa two-phase LSTM model using attention. In: Advances in information retrieval: 40th European conference on IR research, ECIR 2018, Grenoble, France, March 26-29, 2018, proceedings 40. Springer, pp 529–536
  33. Dodds PS, Clark EM, Desu S, Frank MR, Reagan AJ, Williams JR, Mitchell L, Harris KD, Kloumann IM, Bagrow JP et al (2015) Human language reveals a universal positivity bias. Proc Natl Acad Sci 112(8):2389–2394
  34. Egami N, Hinck M, Stewart B, Wei H (2024) Using imperfect surrogates for downstream inference: design-based supervised learning for social science applications of large language models. Adv Neural Inf Process Syst 36
  35. Fan Y, Jiang F (2023) Uncovering the potential of ChatGPT for discourse analysis in dialogue: an empirical study. arXiv preprint arXiv:2305.08391 · arXiv
  36. Frank MR, Mitchell L, Dodds PS, Danforth CM (2013) Happiness and the patterns of life: a study of geolocated tweets. Sci Rep 3(1):2625
  37. Gao J, Zhao H, Yu C, Xu R (2023) Exploring the feasibility of ChatGPT for event extraction. arXiv preprint arXiv:2303.03836 · arXiv
  38. Goebel R, Kano Y, Kim M-Y, Rabelo J, Satoh K, Yoshioka M (2023) Summary of the competition on legal information, extraction/entailment (Coliee) 2023. In: Proceedings of the nineteenth international conference on artificial intelligence and law, pp 472–480
  39. Guo K, Hu A, Mu J, Shi Z, Zhao Z, Vishwamitra N, Hu H (2024) An investigation of large language models for real-world hate speech detection. arXiv preprint arXiv:2401.03346 · arXiv
  40. Hasan MK, Rahman W, Zadeh AB, Zhong J, Tanveer MI, Morency L-P, Hoque ME (2019) Ur-funny: a multimodal language dataset for understanding humor. 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 2046–2056
  41. Hong L, Luo P, Blanco E, Song X (2024) Outcome-constrained large language models for countering hate speech. arXiv preprint arXiv:2403.17146 · arXiv
  42. Hossain T, Logan IV RL, Ugarte A, Matsubara Y, Young S, Singh S (2020) COVIDLies: detecting COVID-19 misinformation on social media. In: Verspoor K, Cohen KB, Conway M, Bruijn B, Dredze M, Mihalcea R, Wallace B (eds.) Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020. Association for Computational Linguistics, Online. https://doi.org/10.18653/v1/2020.nlpcovid19-2.11 . https://aclanthology.org/2020.nlpcovid19-2.11 · DOI
  43. Huang F, Huang Q, Zhao Y, Qi Z, Wang B, Huang Y, Li S (2023) A three-stage framework for event-event relation extraction with large language model. In: International conference on neural information processing. Springer, pp 434–446
  44. Huang G, Liu Z, Van Der Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4700–4708
  45. Hung M, Lauren E, Hon ES, Birmingham WC, Xu J, Su S, Hon SD, Park J, Dang P, Lipsky MS (2020) Social network analysis of COVID-19 sentiments: application of artificial intelligence. J Med Internet Res 22(8):22590
  46. Inácio M, Oliveira HG (2024) Exploring multimodal models for humor recognition in Portuguese. In: Gamallo P, Claro D, Teixeira A, Real L, Garcia M, Oliveira HG, Amaro R (eds.) Proceedings of the 16th international conference on computational processing of Portuguese. Association for Computational Lingustics, Santiago de Compostela, Galicia/Spain, pp 568–574. https://aclanthology.org/2024.propor-1.62
  47. Islam MM, Uddin MA, Islam L, Akter A, Sharmin S, Acharjee UK (2020) Cyberbullying detection on social networks using machine learning approaches. In: 2020 IEEE Asia-Pacific conference on computer science and data engineering (CSDE). IEEE, pp 1–6
  48. Jazayeri SH, Poursaeed A, Najafabadi MO (2023) Social network analysis of green space management actors in Tehran. Int J Geoheritage Parks 11(2):276–285
  49. Jiang J, Ferrara E (2023) Social-LLM: Modeling user behavior at scale using language models and social network data. arXiv preprint arXiv:2401.00893 · arXiv
  50. Kaddour J, Harris J, Mozes M, Bradley H, Raileanu R, McHardy R (2023) Challenges and applications of large language models. arXiv preprint arXiv:2307.10169 · arXiv
  51. Karim MR, Dey SK, Islam T, Shajalal M, Chakravarthi BR (2022) Multimodal hate speech detection from Bengali memes and texts. In: International conference on speech and language technologies for low-resource languages. Springer, pp 293–308
  52. Karisani P, Agichtein E (2018) Did you really just have a heart attack? Towards robust detection of personal health mentions in social media. In: Proceedings of the 2018 world wide web conference, pp 137–146
  53. Kaya M, Fidan G, Toroslu IH (2012) Sentiment analysis of turkish political news. In: 2012 IEEE/WIC/ACM international conferences on web intelligence and intelligent agent technology, vol 1, IEEE, pp 174–180
  54. Kim Y, Suh H, Kim M, Won D, Lee H (2024) KoCoSa: Korean context-aware sarcasm detection dataset. arXiv preprint arXiv:2402.14428 · arXiv
  55. Kreps S, McCain RM, Brundage M (2022) All the news that’s fit to fabricate: AI-generated text as a tool of media misinformation. J Exp Political Sci 9(1):104–117
  56. Lai M, Menini S, Polignano M, Russo V, Sprugnoli R, Venturi G et al (2023) Evalita 2023: Overview of the 8th evaluation campaign of natural language processing and speech tools for Italian. In: Proceedings of the eighth evaluation campaign of natural language processing and speech tools for Italian. Final Workshop (EVALITA 2023), CEUR. Org, Parma, Italy
  57. Lan X, Gao C, Jin D, Li Y (2023) Stance detection with collaborative role-infused LLM-based agents. arXiv preprint arXiv:2310.10467 · arXiv
  58. Lee RK-W, Cao R, Fan Z, Jiang J, Chong W-H (2021) Disentangling hate in online memes. In: Proceedings of the 29th ACM international conference on multimedia, pp 5138–5147
  59. Lee C, Nick B, Brandes U, Cunningham P (2013) Link prediction with social vector clocks. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, pp 784–792
  60. Leite JA, Razuvayevskaya O, Bontcheva K, Scarton C (2023) Detecting misinformation with LLM-predicted credibility signals and weak supervision. arXiv preprint arXiv:2309.07601 · arXiv
  61. 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
  62. Li I, Li Y, Li T, Alvarez-Napagao S, Garcia-Gasulla D, Suzumura T (2020) What are we depressed about when we talk about COVID-19: mental health analysis on tweets using natural language processing. In: Artificial intelligence XXXVII: 40th SGAI international conference on artificial intelligence, AI 2020, Cambridge, UK, December 15–17, 2020, proceedings 40. Springer, pp 358–370
  63. Lin XV, Mihaylov T, Artetxe M, Wang T, Chen S, Simig D, Ott M, Goyal N, Bhosale S, Du J, Pasunuru R, Shleifer S, Koura PS, Chaudhary V, O’Horo B, Wang J, Zettlemoyer L, Kozareva Z, Diab M, Stoyanov V, Li X (2022) Few-shot learning with multilingual generative language models. In: Goldberg Y, Kozareva, Z., Zhang, Y. (eds.) Proceedings of the 2022 conference on empirical methods in natural language processing. Association for Computational Linguistics, Abu Dhabi, United Arab Emirates, pp 9019–9052. https://doi.org/10.18653/v1/2022.emnlp-main.616 . https://aclanthology.org/2022.emnlp-main.616 · DOI
  64. Li Y, Sosea T, Sawant A, Nair AJ, Inkpen D, Caragea C (2021) P-stance: a large dataset for stance detection in political domain. In: Findings of the association for computational linguistics: ACL-IJCNLP 2021, pp 2355–2365
  65. Li J, Tai Z, Zhang R, Yu W, Liu L (2014) Online Bursty event detection from microblog. In: 2014 IEEE/ACM 7th international conference on utility and cloud computing. IEEE. pp 865–870
  66. Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019) Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692 · arXiv
  67. Li S, Yang J, Zhao K (2023) Are you in a masquerade? Exploring the behavior and impact of large language model driven social bots in online social networks. arXiv preprint arXiv:2307.10337 · arXiv
  68. Lyu C, Wu M, Wang L, Huang X, Liu B, Du Z, Shi S, Tu Z (2023) Macaw-LLM: Multi-modal language modeling with image, audio, video, and text integration. arXiv preprint arXiv:2306.09093 · arXiv
  69. Maas A, Daly RE, Pham PT, Huang D, Ng AY, Potts C (2011) Learning word vectors for sentiment analysis. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pp 142–150
  70. Mao J, Liu W (2019) A bert-based approach for automatic humor detection and scoring. In: IberLEF@ SEPLN, pp. 197–202
  71. Ma Z, Yang G, Yang Y, Gao Z, Wang J, Du Z, Yu F, Chen Q, Zheng S, Zhang S et al (2024) An embarrassingly simple approach for LLM with strong ASR capacity. arXiv preprint arXiv:2402.08846 · arXiv
  72. Mohammad S, Kiritchenko S, Sobhani P, Zhu X, Cherry C (2016) Semeval-2016 task 6: Detecting stance in tweets. In: Proceedings of the 10th international workshop on semantic evaluation (SemEval-2016), pp 31–41
  73. Mu Y, Dong C, Bontcheva K, Song X (2024) Large language models offer an alternative to the traditional approach of topic modelling. arXiv preprint arXiv:2403.16248 · arXiv
  74. Naseem U, Thapa S, Zhang Q, Rashid J, Hu L, Nasim M (2023) Temporal tides of emotional resonance: a novel approach to identify mental health on social media. In: Proceedings of the 11th international workshop on natural language processing for social media, pp 1–8
  75. Nasim M, Charbey R, Prieur C, Brandes U (2016) Investigating link inference in partially observable networks: friendship ties and interaction. IEEE Trans Comput Soc Syst 3(3):113–119
  76. Nasim M, Nguyen A, Lothian N, Cope R, Mitchell L (2018) Real-time detection of content polluters in partially observable twitter networks. In: Companion proceedings of the the web conference 2018, pp 1331–1339
  77. Nasim M, Sharif N, Bhandari P, Weber D, Wood M, Falzon L, Kashima Y (2022) Investigating language use by polarised groups on twitter: a case study of the bushfires. In: Proceedings of the 26th Australasian document computing symposium, pp 1–7
  78. Nasim M, Weber D, South T, Tuke J, Bean N, Falzon L, Mitchell L (2022) Are we always in strife? a longitudinal study of the echo chamber effect in the Australian Twittersphere. arXiv preprint arXiv:2201.09161 · arXiv
  79. Negi G, Sarkar R, Zayed O, Buitelaar P (2024) A hybrid approach to aspect based sentiment analysis using transfer learning. arXiv preprint arXiv:2403.17254 · arXiv
  80. Nguyen D, Al Mannai KA, Joty S, Sajjad H, Imran M, Mitra P (2017) Robust classification of crisis-related data on social networks using convolutional neural networks. In: Proceedings of the international AAAI conference on web and social media, vol 11, pp 632–635
  81. Nirmal A, Bhattacharjee A, Sheth P, Liu H (2024) Towards interpretable hate speech detection using large language model-extracted rationales. arXiv preprint arXiv:2403.12403 · arXiv
  82. Parihar AS, Thapa S, Mishra S (2021) Hate speech detection using natural language processing: applications and challenges. In: 2021 5th international conference on trends in electronics and informatics (ICOEI). IEEE, pp 1302–1308
  83. Pauls A, Klein D (2011) Faster and smaller n-gram language models. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, pp 258–267
  84. Perifanos K, Goutsos D (2021) Multimodal hate speech detection in Greek social media. Multimodal Technol Interact 5(7):34
  85. Pilar Salas-Zárate M, Alor-Hernández G, Sánchez-Cervantes JL, Paredes-Valverde MA, García-Alcaraz JL, Valencia-García R (2020) Review of English literature on figurative language applied to social networks. Knowl Inf Syst 62(6):2105–2137
  86. Piskorski J, Stefanovitch N, Nikolaidis N, Da San Martino G, Nakov P (2023) Multilingual multifaceted understanding of online news in terms of genre, framing, and persuasion techniques. In: Proceedings of the 61st annual meeting of the association for computational linguistics (volume 1: long papers), pp 3001–3022
  87. Ponti EM, Glavaš G, Majewska O, Liu Q, Vulić I, Korhonen A (2020) XCOPA: a multilingual dataset for causal commonsense reasoning. In: Webber B, Cohn T, He Y, Liu Y (eds.) Proceedings of the 2020 conference on empirical methods in natural language processing (EMNLP). Association for Computational Linguistics. pp 2362–2376. Online. https://doi.org/10.18653/v1/2020.emnlp-main.185. https://aclanthology.org/2020.emnlp-main.185 · DOI
  88. Pramanick S, Roy A, Patel VM (2022) Multimodal learning using optimal transport for sarcasm and humor detection. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp 3930–3940
  89. Raffel C, Shazeer N, Roberts A, Lee K, Narang S, Matena M, Zhou Y, Li W, Liu PJ (2020) Exploring the limits of transfer learning with a unified text-to-text transformer. J Mach Learn Res 21(140):1–67
  90. Ramakrishnan N, Butler P, Muthiah S, Self N, Khandpur R, Saraf P, Wang W, Cadena J, Vullikanti A, Korkmaz G et al (2014) ’beating the news’ with embers: forecasting civil unrest using open source indicators. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1799–1808
  91. Rauniyar K, Poudel S, Shiwakoti S, Thapa S, Rashid J, Kim J, Imran M, Naseem U (2023) Multi-aspect annotation and analysis of Nepali tweets on anti-establishment election discourse. IEEE Access 11:143092–143115
  92. Rish I et al (2001) An empirical study of the Naive Bayes classifier. In: IJCAI 2001 workshop on empirical methods in artificial intelligence, vol 3, Citeseer, pp 41–46
  93. Ritter A, Mausam Etzioni O, Clark S (2012) Open domain event extraction from twitter. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1104–1112
  94. Romero D, Tan C, Ugander J (2013) On the interplay between social and topical structure. In: Proceedings of the international AAAI conference on web and social media, vol 7, pp 516–525
  95. Rosenthal S, Farra N, Nakov P (2019) Semeval-2017 task 4: sentiment analysis in twitter. arXiv preprint arXiv:1912.00741 · arXiv
  96. Rotstein N, Bensaïd D, Brody S, Ganz R, Kimmel R (2024) FuseCap: Leveraging large language models for enriched fused image captions. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision, pp 5689–5700
  97. Saha P, Agrawal A, Jana A, Biemann C, Mukherjee A (2024) On zero-shot counterspeech generation by LLMS. arXiv preprint arXiv:2403.14938 · arXiv
  98. Sajjad M, Zulifqar F, Khan MUG, Azeem M (2019) Hate speech detection using fusion approach. In: 2019 international conference on applied and engineering mathematics (ICAEM). IEEE, pp 251–255
  99. Sanh V, Debut L, Chaumond J, Wolf T (2019) Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter. arXiv preprint arXiv:1910.01108 · arXiv
  100. Sawhney R, Neerkaje A, Habernal I, Flek L (2023) How much user context do we need? Privacy by design in mental health NLP applications. In: Proceedings of the international AAAI conference on web and social media, vol 17, pp 766–776
  101. Seddari N, Derhab A, Belaoued M, Halboob W, Al-Muhtadi J, Bouras A (2022) A hybrid linguistic and knowledge-based analysis approach for fake news detection on social media. IEEE Access 10:62097–62109
  102. Shah SB, Shiwakoti S, Chaudhary M, Wang H (2024) Memeclip: Leveraging clip representations for multimodal meme classification. arXiv preprint arXiv:2409.14703 · arXiv
  103. Shah SB, Thapa S, Acharya A, Rauniyar K, Poudel S, Jain S, Masood A, Naseem U (2024) Navigating the web of disinformation and misinformation: large language models as double-edged swords. IEEE Access
  104. Shiwakoti S, Thapa S, Rauniyar K, Shah A, Bhandari A, Naseem U (2024) Analyzing the dynamics of climate change discourse on twitter: a new annotated corpus and multi-aspect classification. In: Proceedings of the 2024 joint international conference on computational linguistics, language resources and evaluation (LREC-COLING 2024), pp 984–994
  105. Socher R, Perelygin A, Wu J, Chuang J, Manning CD, Ng AY, Potts C (2013) Recursive deep models for semantic compositionality over a sentiment treebank. In: Proceedings of the 2013 conference on empirical methods in natural language processing, pp 1631–1642
  106. Sun X, Li X, Zhang S, Wang S, Wu F, Li J, Zhang T, Wang G (2023) Sentiment analysis through LLM negotiations. arXiv preprint arXiv:2311.01876 · arXiv
  107. Šuppa M, Skala D, Jašš D, Sučík S, Švec A, Hraška P (2024) Bryndza at climateactivism 2024: stance, target and hate event detection via retrieval-augmented GPT-4 and llama. arXiv preprint arXiv:2402.06549 · arXiv
  108. Thapa S, Jafri F, Hürriyetoğlu A, Vargas F, Lee RK-W, Naseem U (2023) Multimodal hate speech event detection-shared task 4, case 2023. In: Proceedings of the 6th workshop on challenges and applications of automated extraction of socio-political events from text, pp 151–159
  109. Thapa S, Rauniyar K, Jafri F, Shiwakoti S, Veeramani H, Jain R, Kohli GS, Hürriyetoğlu A, Naseem U (2024) Stance and hate event detection in tweets related to climate activism—shared task at CASE 2024. In: Hürriyetoğlu A, Tanev H, Thapa S, Uludoğan G (eds.) Proceedings of the 7th workshop on challenges and applications of automated extraction of socio-political events from text (CASE 2024). Association for Computational Linguistics, St. Julians, Malta, pp. 234–247. https://aclanthology.org/2024.case-1.33
  110. Thapa S, Rauniyar K, Jafri F, Shiwakoti S, Veeramani H, Jain R, Kohli GS, Hürriyetoğlu A, Naseem U (2024) Stance and hate event detection in tweets related to climate activism-shared task at case 2024. In: Proceedings of the 7th workshop on challenges and applications of automated extraction of socio-political events from text (CASE 2024), pp 234–247
  111. Thirunavukarasu AJ, Ting DSJ, Elangovan K, Gutierrez L, Tan TF, Ting DSW (2023) Large language models in medicine. Nat Med 29(8):1930–1940
  112. Tikhonov A, Ryabinin M (2021) It’s all in the heads: using attention heads as a baseline for cross-lingual transfer in commonsense reasoning. In: Zong C, Xia F, Li W, Navigli R (eds.) Findings of the association for computational linguistics: ACL-IJCNLP 2021. Association for Computational Linguistics, pp 3534–3546. Online. https://doi.org/10.18653/v1/2021.findings-acl.310 . https://aclanthology.org/2021.findings-acl.310 · DOI
  113. Tran OT, Dao TT, Dang YN (2021) Stance detection on Vietnamese social media. In: International conference on soft computing and pattern recognition. Springer, pp 75–85
  114. Tuke J, Nguyen A, Nasim M, Mellor D, Wickramasinghe A, Bean N, Mitchell L (2020) Pachinko prediction: A Bayesian method for event prediction from social media data. Inf Process Manage 57(2):102147
  115. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 30
  116. Venkatakrishnan R, Goodarzi M, Canbaz MA (2023) Exploring large language models’ emotion detection abilities: use cases from the middle east. In: 2023 IEEE conference on artificial intelligence (CAI). IEEE, pp 241–244
  117. Walter S, Kinski L, Boda Z (2023) Who talks to whom? using social network models to understand debate networks in the European parliament. Eur Union Politics 24(2):410–423
  118. Wang Z, Yin Z, Argyris YA (2020) Detecting medical misinformation on social media using multimodal deep learning. IEEE J Biomed Health Inform 25(6):2193–2203
  119. Wang H, Lee RK-W (2024) MemeCraft: Contextual and stance-driven multimodal meme generation. arXiv preprint arXiv:2403.14652 · arXiv
  120. Wang L, Xu X, Zhang L, Lu J, Xu Y, Xu H, Zhang C (2024) MMIDR: teaching large language model to interpret multimodal misinformation via knowledge distillation. arXiv preprint arXiv:2403.14171 · arXiv
  121. Weber D, Neumann F (2021) Amplifying influence through coordinated behaviour in social networks. Soc Netw Anal Min 11(1):111
  122. Weber D, Falzon L, Mitchell L, Nasim M (2022) Promoting and countering misinformation during Australia’s 2019–2020 bushfires: a case study of polarisation. Soc Netw Anal Min 12(1):64
  123. Wei J, Bosma M, Zhao VY, Guu K, Yu AW, Lester B, Du N, Dai AM, Le QV (2021) Finetuned language models are zero-shot learners. arXiv preprint arXiv:2109.01652 · arXiv
  124. Wei J, Tay Y, Bommasani R, Raffel C, Zoph B, Borgeaud S, Yogatama D, Bosma M, Zhou D, Metzler D et al (2022) Emergent abilities of large language models. Transactions on Machine Learning Research
  125. Whitehouse C, Choudhury M, Aji AF (2023) LLM-powered data augmentation for enhanced cross-lingual performance. In: The 2023 conference on empirical methods in natural language processing
  126. Wong A, Ho S, Olusanya O, Antonini MV, Lyness D (2021) The use of social media and online communications in times of pandemic COVID-19. J Intensive Care Soc 22(3):255–260
  127. Wu S, Fei H, Qu L, Ji W, Chua T-S (2023) Next-GPT: Any-to-any multimodal LLM. arXiv preprint arXiv:2309.05519 · arXiv
  128. Wu J, Lin H, Yang L, Xu B (2021) Mumor: A multimodal dataset for humor detection in conversations. In: Natural language processing and chinese computing: 10th CCF international conference, NLPCC 2021, Qingdao, China, October 13–17, 2021, proceedings, Part I 10. Springer, pp 619–627
  129. Xia C, Hu J, Zhu Y, Naaman M (2015) What is new in our city? A framework for event extraction using social media posts. In: Advances in knowledge discovery and data mining: 19th Pacific-Asia conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, proceedings, Part I 19. Springer, pp 16–32
  130. Xing F (2024) Designing heterogeneous llm agents for financial sentiment analysis. arXiv preprint arXiv:2401.05799 · arXiv
  131. Yakura H (2023) Evaluating large language models’ ability to understand metaphor and sarcasm using a screening test for Asperger syndrome. arXiv preprint arXiv:2309.10744 · arXiv
  132. Yin D, Bansal H, Monajatipoor M, Li LH, Chang KW (2022) Geomlama: Geo-diverse commonsense probing on multilingual pre-trained language models. In: Proceedings of the 2022 conference on empirical methods in natural language processing, pp 2039–2055 (2022)
  133. Yu S, Da San Martino G, Mohtarami M, Glass J, Nakov P (2021) Interpretable propaganda detection in news articles. In: Proceedings of the international conference on recent advances in natural language processing (RANLP 2021), pp 1597–1605
  134. Zhang X, Chen X, Liu Y, Wang J, Hu Z, Yan R (2024) LLM-driven agents for influencer selection in digital advertising campaigns. arXiv preprint arXiv:2403.15105 · arXiv
  135. Zhang Z, Robinson D, Tepper J (2018) Detecting hate speech on twitter using a convolution-gru based deep neural network. In: The semantic web: 15th international conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, proceedings 15, Springer, pp 745–760
  136. Zhang Y, Sun S, Galley M, Chen Y-C, Brockett C, Gao X, Gao J, Liu J, Dolan WB (2020) Dialogpt: Large-scale generative pre-training for conversational response generation. In: Proceedings of the 58th annual meeting of the association for computational linguistics: system demonstrations, pp 270–278
  137. Zhang B, Yang H, Zhou T, Ali Babar M, Liu X-Y (2023) Enhancing financial sentiment analysis via retrieval augmented large language models. In: Proceedings of the fourth ACM international conference on AI in finance, pp 349–356
  138. Zhang X, Zhao J, LeCun Y (2015) Character-level convolutional networks for text classification. Adv Neural Inf Process Syst 28
  139. Zhao H, Chen H, Yang F, Liu N, Deng H, Cai H, Wang S, Yin D, Du M (2024) Explainability for large language models: a survey. ACM Trans Intell Syst Technol 15(2):1–38
  140. Zhong S, Huang Z, Gao S, Wen W, Lin L, Zitnik M, Zhou P (2023) Let’s think outside the box: Exploring leap-of-thought in large language models with creative humor generation. arXiv preprint arXiv:2312.02439 · arXiv
  141. Thapa, S., Shiwakoti, S., Shah, S.B. et al. Large language models (LLM) in computational social science: prospects, current state, and challenges. Soc. Netw. Anal. Min. 15, 4 (2025). https://doi.org/10.1007/s13278-025-01428-9

Cited by

Sorted by citations

Authors
Thapa, S., Shiwakoti, S., Shah, S. B., Adhikari, S., Veeramani, H., Nasim, M., & Naseem, U.
Affiliations
  • Virginia Tech (US)
  • Northeastern University (US)
  • Columbia University (US)
  • University of California, Los Angeles (US)
  • The University of Western Australia (AU)
  • Macquarie University (AU)
Corresponding author
Thapa, Surendrabikram
ORCID
Venue
Social Network Analysis and Mining
Publisher
Springer Science and Business Media LLC
Published
2025-03-09
Volume
15
Issue
1
ISSN
  • 1869-5450 (ISSN-L)
  • 1869-5469
DOI
10.1007/s13278-025-01428-9
OpenAlex
W4408259027
Cited by
74
References
11
Citations by year
2026: 37 · 2025: 37
FWCI
104.38
Type
journal article
Language
en
Open access
Licence
Locations
  • Social Network Analysis and Mining (publishedVersion, cc-by)
  • UWA Profiles and Research Repository (UWA) (publishedVersion, cc-by)
Topics
  • Social Sciences › General Social Sciences › Computational and Text Analysis Methods
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
large language models, computational social science, sentiment analysis, hate speech detection, stance detection, humor detection, misinformation detection, event understanding, social network analysis, data bias, privacy, ethical use of AI, responsible AI

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review