TimeLMs: Diachronic Language Models from Twitter

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515

Transformers: State-of-the-Art Natural Language Processing

Wolf, T., Debut, L., Sanh, V., Chaumond, J., Delangue, C., Moi, A., Cistac, P., Rault, T., Louf, R., Funtowicz, M., et al. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2020.emnlp-demos.6 · Cited by 8,741

BioBERT: a pre-trained biomedical language representation model for biomedical text mining

Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C. H., & Kang, J. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz682 · Cited by 7,218

GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding

Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., & Bowman, S. (2018) · Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP · doi:10.18653/v1/w18-5446 · Cited by 4,222

SciBERT: A Pretrained Language Model for Scientific Text

Beltagy, I., Lo, K., & Cohan, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1371 · Cited by 3,218

Domain-specific language model pretraining for biomedical natural language processing

Gu, Y., Tinn, R., Cheng, H., Lucas, M., Usuyama, N., Liu, X., Naumann, T., Gao, J., & Poon, H. (2022) · ACM Transactions on Computing for Healthcare · doi:10.1145/3458754 · Cited by 2,170

On the resemblance and containment of documents

Broder, A. (2002) · Compression and Complexity of Sequences (SEQUENCES) · doi:10.1109/sequen.1997.666900 · Cited by 1,818

Gradient Episodic Memory for Continual Learning

Lopez-Paz, D., & Ranzato, M. (2017) · arXiv · doi:10.48550/arxiv.1706.08840 · Cited by 1,403

Diachronic Word Embeddings Reveal Statistical Laws of Semantic Change

Hamilton, W. L., Leskovec, J., & Jurafsky, D. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1141 · Cited by 1,170

ChatGPT: Jack of all trades, master of none

Kocoń, J., Cichecki, I., Kaszyca, O., Kochanek, M., Szydło, D., Baran, J., Bielaniewicz, J., Gruza, M., Janz, A., Kanclerz, K., et al. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101861 · Cited by 758

A multimodal approach to cross-lingual sentiment analysis with ensemble of transformer and LLM

Miah, M. S. U., Kabir, M. M., Sarwar, T. B., Safran, M., Alfarhood, S., & Mridha, M. F. (2024) · Scientific Reports · doi:10.1038/s41598-024-60210-7 · Cited by 210

TweetNLP: Cutting-Edge Natural Language Processing for Social Media

Camacho-collados, J., Rezaee, K., Riahi, T., Ushio, A., Loureiro, D., Antypas, D., Boisson, J., Espinosa Anke, L., Liu, F., & Martínez Cámara, E. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · doi:10.18653/v1/2022.emnlp-demos.5 · Cited by 142

AI Psychometrics: Assessing the Psychological Profiles of Large Language Models Through Psychometric Inventories

Pellert, M., Lechner, C. M., Wagner, C., Rammstedt, B., & Strohmaier, M. (2024) · Perspectives on Psychological Science · doi:10.1177/17456916231214460 · Cited by 97

Generative language models exhibit social identity biases

Hu, T., Kyrychenko, Y., Rathje, S., Collier, N., van der Linden, S., & Roozenbeek, J. (2024) · Nature Computational Science · doi:10.1038/s43588-024-00741-1 · Cited by 100

TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations at Twitter

Zhang, X., Malkov, Y. A., Florez, O. U., Park, S., McWilliams, B., Han, J., & El-Kishky, A. (2023) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3580305.3599921 · Cited by 116

Bias against 93 stigmatized groups in masked language models and downstream sentiment classification tasks

Mei, K., Fereidooni, S., & Caliskan, A. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594109 · Cited by 56

Towards Lifelong Learning of Large Language Models: A Survey

Zheng, J., Qiu, S., Shi, C., & Ma, Q. (2025) · ACM Computing Surveys · doi:10.1145/3716629 · Cited by 93

Using Twitter data to understand public perceptions of approved versus off-label use for COVID-19-related medications

Hua, Y., Jiang, H., Lin, S., Yang, J., Plasek, J. M., Bates, D. W., & Zhou, L. Q. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac114 · Cited by 45

Understanding the sentiment associated with cultural ecosystem services using images and text from social media

Havinga, I., Marcos, D., Bogaart, P. W., Tuia, D., & Hein, L. (2023) · Ecosystem Services · doi:10.1016/j.ecoser.2023.101581 · Cited by 50

TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models

Jang, J., Ye, S., Lee, C.-H., Yang, S., Shin, J., Han, J., Kim, G., & Seo, M. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.418 · Cited by 103

Crypto-influencers

Merkley, K. J., Pacelli, J., Piorkowski, M., & Williams, B. M. (2024) · Review of accounting studies · doi:10.1007/s11142-024-09838-4 · Cited by 35

Temporal Attention for Language Models

Rosin, G. D., & Radinsky, K. (2022) · Findings of the Association for Computational Linguistics: NAACL · doi:10.18653/v1/2022.findings-naacl.112 · Cited by 48

Continual pre-training mitigates forgetting in language and vision

Cossu, A., Carta, A., Passaro, L., Lomonaco, V., Tuytelaars, T., & Bacciu, D. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106492 · Cited by 57

Deep learning and NLP in cryptocurrency forecasting: Integrating financial, blockchain, and social media data

Gurgul, V., Lessmann, S., & Härdle, W. K. (2025) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2025.02.007 · Cited by 53

Polarity-Based Sentiment Analysis of Georeferenced Tweets Related to the 2022 Twitter Acquisition

Schmidt, S., Zorenböhmer, C., Arifi, D., & Resch, B. (2023) · Information · doi:10.3390/info14020071 · Cited by 28

Enhancing Low-Resource Sentiment Analysis: A Transfer Learning Approach

Daneshfar, F. (2024) · passer · doi:10.24271/psr.2024.440793.1484 · Cited by 31

Using large language models to assess public perceptions around glucagon-like peptide-1 receptor agonists on social media

Somani, S. S., Jain, S. S., Sarraju, A., Sandhu, A. T., Hernandez‐Boussard, T., & Rodríguez, F. (2024) · Communications Medicine · doi:10.1038/s43856-024-00566-z · Cited by 34

A Space-Time Framework for Sentiment Scope Analysis in Social Media

Bonifazi, G., Cauteruccio, F., Corradini, E., Marchetti, M., Sciarretta, L., Ursino, D., & Virgili, L. (2022) · Big Data and Cognitive Computing · doi:10.3390/bdcc6040130 · Cited by 30

Discovering sentiment insights: streamlining tourism review analysis with Large Language Models

Guidotti, D., Pandolfo, L., & Pulina, L. (2025) · Information Technology & Tourism · doi:10.1007/s40558-024-00309-9 · Cited by 38

Multi-source multi-modal domain adaptation

Zhao, S.-C., Jiang, J., Tang, W.-B., Zhu, J.-K., Chen, H., Xu, P.-F., Schuller, B. W., Tao, J.-H., Yao, H.-X., & Ding, G.-G. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102862 · Cited by 28

Public attitudes toward chatgpt on twitter: sentiments, topics, and occupations

Koonchanok, R., Pan, Y., & Jang, H. (2024) · Social Network Analysis and Mining · doi:10.1007/s13278-024-01260-7 · Cited by 25

The Qatar World Cup and Twitter sentiment: Unraveling the interplay of soft power, public opinion, and media scrutiny

Hassan, A. A. M., & Wang, J. (2023) · International Review for the Sociology of Sport · doi:10.1177/10126902231218700 · Cited by 24

Clustering-Based Joint Topic-Sentiment Modeling of Social Media Data: A Neural Networks Approach

Hanny, D., & Resch, B. (2024) · Information · doi:10.3390/info15040200 · Cited by 24

Signaling theory in entrepreneurial fundraising and crowdfunding research

Steigenberger, N., Garz, M., & Cyron, T. (2024) · Journal of Small Business Management · doi:10.1080/00472778.2024.2412710 · Cited by 30

Unlocking language barriers: Assessing pre-trained large language models across multilingual tasks and unveiling the black box with Explainable Artificial Intelligence

Kastrati, M., Imran, A. S., Hashmi, E., Kastrati, Z., Daudpota, S. M., & Biba, M. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110136 · Cited by 25

GPT-4 as an X data annotator: Unraveling its performance on a stance classification task

Liyanage, C. R., Gokani, R., & Mago, V. (2024) · PLoS ONE · doi:10.1371/journal.pone.0307741 · Cited by 23

A framework for investigating the dynamics of user and community sentiments in a social platform

Bonifazi, G., Cauteruccio, F., Corradini, E., Marchetti, M., Giorgio Terracina, B., Domenico Ursino, A., & Virgili, L. (2023) · Data & Knowledge Engineering · doi:10.1016/j.datak.2023.102183 · Cited by 20

TweetFinSent: A Dataset of Stock Sentiments on Twitter

Pei, Y., Mbakwe, A. B., Gupta, A., Alamir, S., Lin, H., Liu, X., & Shah, S. (2022) · Workshop on Financial Technology and Natural Language Processing (FinNLP) · doi:10.18653/v1/2022.finnlp-1.5 · Cited by 19

Fairness and social bias quantification in Large Language Models for sentiment analysis

Radaideh, M. I., Kwon, O. H., & Radaideh, M. I. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113569 · Cited by 24

Investigating the effectiveness of Twitter sentiment in cryptocurrency close price prediction by using deep learning

Amirshahi, B., & Lahmiri, S. (2023) · Expert Systems · doi:10.1111/exsy.13428 · Cited by 21

Proud Boys on Telegram

Zhong, W., Bailard, C. S., Broniatowski, D. A., & Tromble, R. (2024) · Journal of Quantitative Description Digital Media · doi:10.51685/jqd.2024.003 · Cited by 20

Exploring the potential of large-language models (LLMs) for student feedback sentiment analysis

Shaikh, S., Daudpota, S. M., Yayilgan, S. Y., & Sindhu, S. (2023) · International Conference on Frontiers of Information Technology (FIT) · doi:10.1109/fit60620.2023.00047 · Cited by 23

Framing fact-checks as a “confirmation” increases engagement with corrections of misinformation: a four-country study

Aruguete, N., Batista, F., Calvo, E. F., Altube, M. G., Scartascini, C., & Ventura, T. (2024) · Scientific Reports · doi:10.1038/s41598-024-53337-0 · Cited by 18

Sentiment as a shipping market predictor: Testing market-specific language models

Sui, C., Wang, S., & Zheng, W. (2024) · Transportation Research Part E Logistics and Transportation Review · doi:10.1016/j.tre.2024.103651 · Cited by 18

Public Attitudes Toward ChatGPT on Twitter: Sentiments, Topics, and OccupationsPreprint

Koonchanok, R., Pan, Y., & Jang, H. (2024) · Research Square · doi:10.21203/rs.3.rs-3945065/v1 · Cited by 16

Characterizing Information Propagation in Fringe Communities on Telegram

Hoseini, M., Melo, P. D. F., Benevenuto, F., Feldmann, A., & Zannettou, S. (2024) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31336 · Cited by 15

Experience is all you need: a large language model application of fine-tuned GPT-3.5 and RoBERTa for aspect-based sentiment analysis of college football stadium reviews

Qian, T. Y., Li, W., Gong, H., Seifried, C. S., & Xu, C. (2024) · Sport Management Review · doi:10.1080/14413523.2024.2386467 · Cited by 16

Continual Pre-Training Mitigates Forgetting in Language and VisionPreprint

Cossu, A., Carta, A., Passaro, L., Lomonaco, V., Tuytelaars, T., & Bacciu, D. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4495233 · Cited by 15

Enhancing sustainability reporting practices in the notebook manufacturing industry: a multifaceted analysis integrating traditional reports and social media data

Maghsoudi, M., Shokouhyar‎, S., Sanaee, N., & Shokoohyar, S. (2024) · Annals of Operations Research · doi:10.1007/s10479-024-06343-4 · Cited by 13

Themes and sentiments in conversations about food waste on Twitter: Proposal of a framework using neural topic modeling

Barbosa, M. W., & Gomes, A. (2024) · Food Quality and Preference · doi:10.1016/j.foodqual.2024.105311 · Cited by 13

M3A: A multimodal misinformation dataset for media authenticity analysis

Xu, Q., Chen, H., Du, H., Zhang, H., Łukasik, S., Zhu, T., & Yu, X. (2024) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2024.104205 · Cited by 16

Enhanced twitter sentiment analysis with dual joint classifier integrating RoBERTa and BERT architectures

He, L. (2024) · Frontiers in Physics · doi:10.3389/fphy.2024.1477714 · Cited by 16

Discovering Influencers in Opinion Formation Over Social Graphs

Shumovskaia, V., Kayaalp, M., Cemri, M., & Sayed, A. H. (2023) · IEEE Open Journal of Signal Processing · doi:10.1109/ojsp.2023.3261132 · Cited by 12

Infusing Affective Computing Models into Advertising Research on Emotions

Wen, T. J., Chuan, C., Anghelcev, G., Sar, S., Yun, J. T., & Xu, Y. (2024) · Journal of Advertising · doi:10.1080/00913367.2024.2409254 · Cited by 13

Overview of the CLEF-2023 LongEval Lab on Longitudinal Evaluation of Model Performance

Alkhalifa, R., Bilal, I., Borkakoty, H., Camacho-Collados, J., Deveaud, R., El-Ebshihy, A., Espinosa-Anke, L., González-Sáez, G., Galuščáková, P., Goeuriot, L., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-42448-9_28 · Cited by 12

Contextualizing gender disparities in online teaching evaluations for professors

Zheng, X. M., Vastrad, S., Hu, J., & Ni, C. (2023) · PLoS ONE · doi:10.1371/journal.pone.0282704 · Cited by 11

#Canceled! Exploring the phenomenon of canceling

Cummings, K. H., Zafari, B., & Beitelspacher, L. S. (2024) · Journal of Business Research · doi:10.1016/j.jbusres.2024.115025 · Cited by 11

SentiEntityRec: Entity-level sentiment perception graph neural network for news recommendation

Wang, Q., Wang, J., & Ibrahim, N. F. (2025) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-025-00087-2 · Cited by 11

Do CoNLL-2003 Named Entity Taggers Still Work Well in 2023?

Liu, S., & Ritter, A. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.459 · Cited by 12

A Hybrid Method of Sentiment Analysis and Machine Learning Algorithm for the U.S. Presidential Election Forecasting

Feng, G., Chen, K., Cai, H., & Li, Z. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386950 · Cited by 11

COVID-19 Public Health Communication on X (Formerly Twitter): Cross-Sectional Study of Message Type, Sentiment, and Source

Parveen, S., Pereira, A. G., Orjuela, N. G., McHugh, P., Surendran, A., Vornhagen, H., & Vellinga, A. (2024) · JMIR Formative Research · doi:10.2196/59687 · Cited by 10

Toward satisfactory public accessibility: A crowdsourcing approach through online reviews to inclusive urban design

Li, L., Hu, S., Dai, Y., Deng, M., Momeni, P., Laverghetta, G., Fan, L., Ma, Z., Wang, X., Ma, S., et al. (2025) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2025.102329 · Cited by 15

Benefits of Adopting Wild Pedagogies in University Education

Krigstin, S., Cardoso, J., Kayadapuram, M., & Wang, M. L. (2023) · Forests · doi:10.3390/f14071375 · Cited by 12

Landscape of Large Language Models in Global English News: Topics, Sentiments, and Spatiotemporal Analysis

Xian, L., Li, L., Xu, Y., Zhang, Z., & Hemphill, L. (2024) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31416 · Cited by 10

Accelerating Aggregation Queries on Unstructured Streams of Data

Russo, M., Hashimoto, T., Kang, D. D., Sun, Y. J., & Zaharia, M. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3611479.3611496 · Cited by 10

Off-label drug use during the COVID-19 pandemic in Africa: topic modelling and sentiment analysis of ivermectin in South Africa and Nigeria as a case study

Nia, Z. M., Bragazzi, N. L., Ahamadi, A., Asgary, A., Garcia, B. R. M., Orbinski, J., Seyyed-Kalantari, L., Woldegerima, W. A., Wu, J., & Kong, J. D. (2023) · Journal of The Royal Society Interface · doi:10.1098/rsif.2023.0200 · Cited by 9

Leveraging GPT for the Generation of Multi-Platform Social Media Datasets for Research

Tari, H., Khan, M. D., Rutten, J., Othman, D., Bertaglia, T., Kaushal, R., & Iamnitchi, A. (2024) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/3648188.3675153 · Cited by 10

A Multi-Level Embedding Framework for Decoding Sarcasm Using Context, Emotion, and Sentiment Feature

Najafabadi, M. K., Ko, T. Z. C., Chaeikar, S. S., & Shabani, N. (2024) · Electronics · doi:10.3390/electronics13224429 · Cited by 9

LineConGraphs : Line Conversation Graphs for Effective Emotion Recognition Using Graph Neural Networks

Krishnan, G. S., Padi, S., Greenberg, C. S., Ravindran, B., Manocha, D., & Sriram, R. D. (2025) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2025.3537538 · Cited by 9

RetroSketch: A Retrospective Method for Measuring Emotions and Presence in Virtual Reality

Potts, D., Gada, M., Gupta, A., Goel, K., Krzok, K. P., Pate, G., Hartley, J. P., Weston-Arnold, M., Aylott, J., Clarke, C., et al. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713957 · Cited by 12

Hashtag-Guided Low-Resource Tweet Classification

Diao, S., Keh, S. S., Pan, L., Tian, Z., Song, Y., & Zhang, T. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583194 · Cited by 11

SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research

Antypas, D., Ushio, A., Barbieri, F., Neves, L., Rezaee, K., Espinosa-Anke, L., Pei, J., & Camacho-Collados, J. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.838 · Cited by 11

Proceedings of the 4th Workshop on Gender Bias in Natural Language Processing (GeBNLP)

(2022) · doi:10.18653/v1/2022.gebnlp-1 · Cited by 9

Multimodal GeoAI: An integrated spatio-temporal topic-sentiment model for the analysis of geo-social media posts for disaster management

Hanny, D., & Resch, B. (2025) · International Journal of Applied Earth Observation and Geoinformation · doi:10.1016/j.jag.2025.104540 · Cited by 10

#creativity: Exploring Lay Conceptualizations of Creativity with Twitter Hashtags

Ceh, S. M., Christensen, A. P., Lebuda, I., & Benedek, M. (2023) · Creativity Research Journal · doi:10.1080/10400419.2023.2214472 · Cited by 8

Continual Learning Under Language Shift

Gogoulou, E., Lesort, T., Boman, M., & Nivre, J. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70563-2_6 · Cited by 23

IndoGovBERT: A Domain-Specific Language Model for Processing Indonesian Government SDG Documents

Riyadi, A., Kovacs, M., Serdült, U., & Kryssanov, V. V. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8110153 · Cited by 10

Language-Based Mixture of Transformers for Sexism Identification in Social Networks

Petrescu, A., Truică, C., & Apostol, E. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04354-2_10 · Cited by 9

On the Impact of Temporal Concept Drift on Model Explanations

Zhao, Z., Chrysostomou, G., Bontcheva, K., & Αλέτρας, Ν. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.298 · Cited by 13

Large Language Model-Guided Disentangled Belief Representation Learning on Polarized Social Graphs

Li, J., Han, R., Sun, C., Sun, D., Wang, R., Zeng, J., Yan, Y., Tong, H., & Abdelzaher, T. (2024) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn61486.2024.10637650 · Cited by 7

Performance unfairness of large language models in cross-language fact-checking

Wang, D., Tsang, S. J., & Zhou, Y. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.104616 · Cited by 7

Pre-trained Speech Processing Models Contain Human-Like Biases that Propagate to Speech Emotion Recognition

Slaughter, I., Greenberg, C. S., Schwartz, R., & Caliskan, A. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.602 · Cited by 12

Unveiling Global Narratives: A Multilingual Twitter Dataset of News Media on the Russo-Ukrainian Conflict

Hakimov, S., & Cheema, G. S. (2024) · International Conference on Multimedia Retrieval · doi:10.1145/3652583.3657622 · Cited by 9

Sentiment Analysis for Women in STEM using Twitter and Transfer Learning Models

Fouad, S., & Alkooheji, E. (2023) · IEEE International Conference on Semantic Computing (ICSC) · doi:10.1109/icsc56153.2023.00045 · Cited by 7

Human behavior analysis on political retweets using machine learning algorithms

Patel, H., Kansara, A., Prathap, B. R., & Kumar, K. P. (2023) · Measurement Sensors · doi:10.1016/j.measen.2023.100768 · Cited by 7

TEDB System Description to a Shared Task on Euphemism Detection 2022

Wiriyathammabhum, P. (2022) · Workshop on Figurative Language Processing (FigLang) · doi:10.18653/v1/2022.flp-1.1 · Cited by 7

GPT-4 as a Twitter Data Annotator: Unraveling Its Performance on a Stance Classification TaskPreprint

Liyanage, C., Gokani, R., & Mago, V. (2023) · doi:10.36227/techrxiv.24143706.v1 · Cited by 7

Comparing information completeness in brand-related user-generated content media

Park, S., & Lee, S. (2025) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2025.124048 · Cited by 6

Using Natural Language Processing to Explore Patient Perspectives on AI Avatars in Support Materials for Patients With Breast Cancer: Survey Study

Cheese, E., Bichoo, R. A., Grover, K., Dumitru, D., Zenonos, A., Groark, J., Gibson, D. F. C., & Pope, R. A. (2025) · Journal of Medical Internet Research · doi:10.2196/70971 · Cited by 8

Why Majorities Are Silent but Minorities Are Loud: An Empirical Approach to Opinion Interactions in Online Communities

Ha, T., & Lee, S. (2023) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2023.2233123 · Cited by 6

A Step Towards Modern Disinformation Detection: Novel Methods for Detecting LLM-Generated Text

Nathanson, S., Yoo, Y., Na, D., Cao, Y., & Watkins, L. A. (2024) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom61039.2024.10773838 · Cited by 7

The Problem of Semantic Shift in Longitudinal Monitoring of Social Media

Harrigian, K., & Dredze, M. H. (2022) · ACM Web Science Conference (WebSci) · doi:10.1145/3501247.3531566 · Cited by 9

Public Attitudes Toward ChatGPT on Twitter: Sentiments, Topics, and Occupations

Koonchanok, R., Pan, Y., & Jang, H. (2023) · arXiv · doi:10.48550/arxiv.2306.12951 · Cited by 5

Stay Tuned: Improving Sentiment Analysis and Stance Detection Using Large Language Models

Griswold, M., Robbins, M. W., & Pollard, M. (2025) · Political Analysis · doi:10.1017/pan.2025.10023 · Cited by 6

Examining Discussions Related to Transgender Athletes on Twitter: The Role of Negative Emotion on Message Engagement

Calabrese, C., Yu, X., & Oh, Y. J. (2024) · Communication & Sport · doi:10.1177/21674795241265969 · Cited by 5

Development of a natural language processing algorithm to extract social determinants of health from clinician notes

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