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

The Emotions of the Crowd: Learning Image Sentiment from Tweets via Cross-Modal Distillation

Serra, A., Carrara, F., Tesconi, M., & Falchi, F. (2023) · Frontiers in artificial intelligence and applications · doi:10.3233/faia230503 · Cited by 4

Analyzing Public Sentiment on Social Media during FIFA World Cup 2022 using Deep Learning and Explainable AI

Arnob, S. S., Shikder, M. A. A., Ovey, T. A., Rhythm, E. R., & Rasel, A. A. (2023) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit60459.2023.10441156 · Cited by 4

Enhancing Sentiment Analysis of Persian Tweets: A Transformer-Based Approach

Vakili, Y. Z., Fallah, A., & Zakeri, S. (2024) · International Conference on Web Research (ICWR) · doi:10.1109/icwr61162.2024.10533353 · Cited by 4

Striking the Balance: Evaluating Content Quality and Reward Dynamics in Blockchain Online Social Media

Mancino, D., Guidi, B., Michienzi, A., & Viviani, M. (2025) · IEEE Access · doi:10.1109/access.2025.3536205 · Cited by 5

Learning Dynamic Contextualised Word Embeddings via Template-based Temporal Adaptation

Tang, X., Zhou, Y. X., & Bollegala, D. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.520 · Cited by 4

Dynamic Benchmarking of Masked Language Models on Temporal Concept Drift with Multiple Views

Margatina, K., Wang, S., Vyas, Y., John, N. A., Benajiba, Y., & Ballesteros, M. (2023) · Conference of the European Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2023.eacl-main.211 · Cited by 9

Social Learning in Community Structured Graphs

Shumovskaia, V., Kayaalp, M., & Sayed, A. H. (2024) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2024.3401418 · Cited by 4

Bilingual Dialogue Dataset with Personality and Emotion Annotations for Personality Recognition in Education

Liu, Z., Xiao, Y., Su, Z. M., Ye, L., Lu, K., & Peng, X. (2025) · Scientific Data · doi:10.1038/s41597-025-04836-w · Cited by 4

Gender-Based Violence as a Cyberthreat: The Impact of Social Media on Digital Security

Zambrano, P., Torres, J., Anchundia, C. E., & Illicachi, J. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-94953-1_20 · Cited by 3

Topic and Sentiment Trends in Semaglutide Discussions on X: Subpopulation-Based Longitudinal Analysis

Momeni, P., Laverghetta, G., Ligatti, J., & Li, L. (2025) · Online Journal of Public Health Informatics · doi:10.2196/80660 · Cited by 3

PsyLink: User Identity Linkage via Psychological Characteristic Modeling

Ma, X., Qiu, H., Li, S., Yao, S., Лю, Б., & Lin, W. (2023) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/icpads60453.2023.00208 · Cited by 3

CommentVis: Unveiling Comment Insights Through Interactive Visualization Tool

Yan, G., Jang, J., & Seo, J. (2024) · IEEE Pacific Visualization Conference (PacificVis) · doi:10.1109/pacificvis60374.2024.00050 · Cited by 3

SentKB-BERT: Sentiment-filtered Knowledge-based Stance Detection

Chen, H., Yan, K., Kadhim, M. R., Wu, K., & Tian, L. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651178 · Cited by 4

Detecting Online Sexism: Integrating Sentiment Analysis with Contextual Language Models

Belbachir, F., Roustan, T., & Soukane, A. (2024) · AI · doi:10.3390/ai5040137 · Cited by 3

Decoding Consumer Sentiments and Emotions in the Metaverse

Shin, E., & Miller, C. A. (2025) · International Journal of Consumer Studies · doi:10.1111/ijcs.70053 · Cited by 4

Exploring Culturally Informed AI Assistants: A Comparative Study of ChatBlackGPT and ChatGPT

Egede, L., Haque, E. A., Thompson, G., Boyd, A. E., Smith, A. D. R., & Johnson, B. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3720136 · Cited by 4

The interaction between emotion dynamics and opinion changes in the era of generative AI

Li, S., Fan, S., & Demartini, G. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100722 · Cited by 5

Opportunities and challenges of integrating ChatGPT in education: Sentiment analysis and topic modeling

Teerakapibal, S., & Kusawat, P. (2025) · Journal of Education for Business · doi:10.1080/08832323.2025.2536255 · Cited by 4

Using Twitter Dataset for Social Listening in Singapore

Wang, Q., Sailor, H. B., Lee, K. A., Ma, K., Goh, K. H., & Boh, W. F. (2024) · IEEE Access · doi:10.1109/access.2024.3427760 · Cited by 3

Evaluating Ensembled Transformers for Multilingual Code-Switched Sentiment Analysis

Aryal, S. K., Prioleau, H., Washington, G. J., & Burge, L. (2023) · International Conference on Computational Science and Computational Intelligence (CSCI) · doi:10.1109/csci62032.2023.00032 · Cited by 3

Stock prediction using Bi-LSTM and RoBERTa sentiment analysis

Wang, Y. (2024) · Applied and Computational Engineering · doi:10.54254/2755-2721/69/20241474 · Cited by 5

Predicting Stock Price Volatility Amid Controversies: Harnessing News, Tweets, and Google Search Trends with CNN and LSTM

Mescioglu, B., & Abuomar, O. “. (2023) · IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon59035.2023.10316142 · Cited by 3

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 · Cited by 3

Sentiment Analysis and Emotion Detection of COVID-19 Geo-Tagged Twitter Data

Wang, S., Dhakal, S., & Upadhyay, B. P. (2024) · IEEE International Conference on Big Data Analytics (ICBDA) · doi:10.1109/icbda61153.2024.10607368 · Cited by 3

Assessing the validity of wearable technology as game input for exergames

Henry, J., Kendrick, C., Cui, X., & Arnab, S. (2024) · SeGAH ... IEEE ... International Conference on Serious Games and Applications for Health/IEEE International Conference on Serious Games and Applications for Health · doi:10.1109/segah61285.2024.10639589 · Cited by 2

Sentiment analysis and summarization with ChatGPT: implications for sales prediction

Li, Z., Chan, F. Y., Gao, C., Tong, C., & Ye, Q. (2025) · Journal of Digital Management · doi:10.1007/s44362-025-00008-3 · Cited by 2

Aligning Emotions: A Comparative Analysis of Text and Imagery by European Party Leaders on Instagram

Decadri, S., Mancosu, M., Negri, F., & Scaduto, G. (2025) · The International Journal of Press/Politics · doi:10.1177/19401612251339107 · Cited by 3

Research on the Enhanced DistilBERT Model for NLP-Based Text Analysis

Wang, D., Long, Y., & Huang, X. (2025) · Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS) · doi:10.1109/acctcs66275.2025.00124 · Cited by 2

Quantum technologies and geopolitics: comparing parliamentary rhetoric

Suter, V., Pöhlmann, G., Ma, C., & Meckel, M. (2025) · EPJ Quantum Technology · doi:10.1140/epjqt/s40507-025-00456-w · Cited by 2

Towards High-Fidelity Synthetic Multi-Platform Social Media Datasets via Large Language Models

Tari, H., Sereiva, N., Kaushal, R., Bertaglia, T., & Iamnitchi, A. (2026) · ACM Transactions on the Web · doi:10.1145/3789496 · Cited by 5

An emotion aware context adaptive machine learning approach for detecting hate speech in social media

Chavinda, K., Kalansooriya, P., Weerasuriya, T., de Silva, N., Thayasivam, U., Srikandabala, K., Prabagar, K., & Alahakoon, D. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-12029-8 · Cited by 2

From pixels to perception: fine-tuning vision-language models for semantic urban perception

Huang, G., Song, X., & Jiao, H. (2026) · International Journal of Digital Earth · doi:10.1080/17538947.2026.2677402 · Cited by 2

HPNewsRec: Hybrid perception entity-centric personalized news recommendation

Wang, Q., Wang, J., Zhang, Y., Cao, S., Yang, X., & Ibrahim, N. F. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00919-9 · Cited by 3

Information Extraction from Social Media

Mishra, S., Rezapour, R., & Diesner, J. (2022) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3511808.3557503 · Cited by 2

An Example of (Too Much) Hyper-Parameter Tuning In Suicide Ideation Detection

Schoene, A. M., Ortega, J. E., Amir, S., & Church, K. (2023) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v17i1.22227 · Cited by 2

Temporal Word Meaning Disambiguation using TimeLMs

Godbole, M. S., Dandavate, P., & Kane, A. (2022) · doi:10.18653/v1/2022.evonlp-1.8 · Cited by 3

A Word Sense Distribution-based approach for Semantic Change Prediction

Tang, X., Zhou, Y., Aida, T., Sen, P., & Bollegala, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.231 · Cited by 4

Xenophobia Meter: Defining and Measuring Online Sentiment toward Foreigners on Twitter

Umarova, K., Okorafor, O., Lu, P., Shan, S., Xu, A., Zhou, R., Otiono, J., Lyon, B., & Leshed, G. (2024) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31406 · Cited by 2

Covid19-twitter: A Twitter-based Dataset for Discourse Analysis in Sentence-level Sentiment Classification

Gupta, S., Bouadjenek, M. R., Robles‐Kelly, A., Lee, T.-K., Nguyen, T. T., Nazari, A., & Thiruvady, D. (2024) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3627673.3679120 · Cited by 2

Generative AI vs. Human Deception: A Comparative Analysis of ChatGPT, Gemini, and Human-Generated Disinformation

Mason, C., AlEroud, A. F., Melhem, A., Halloush, Z., & Williams, J. A. (2025) · ACM International Workshop on Security and Privacy Analytics (IWSPA) · doi:10.1145/3716815.3729013 · Cited by 2

Optimizing Social Media Sentiment Analysis: RoBERTa Framework for Evaluating Public Perception of DeepSeek

Santosa, A., Prasanna, G. A., Rizky, M., & Prabowo, A. S. (2025) · International Conference on Electronics Representation and Algorithm (ICERA) · doi:10.1109/icera66156.2025.11087264 · Cited by 2

Sentiment Analysis of Tweets Using Deep Learning

Ranganathan, J., & Tsahai, T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-22064-7_9 · Cited by 2

Measuring the Impact of Language Models in Sentiment Analysis for Mexico’s COVID-19 Pandemic

León-Sandoval, E., Zareei, M., Barbosa-Santillán, L. I., & Falcón-Morales, L. E. (2022) · Electronics · doi:10.3390/electronics11162483 · Cited by 3

Swap and Predict – Predicting the Semantic Changes in Words across Corpora by Context Swapping

Aida, T., & Bollegala, D. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.520 · Cited by 3

MMOOC: A Multimodal Misinformation Dataset for Out-of-Context News Analysis

Xu, Q., Du, H., Chen, H., Liu, B., & Yu, X. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-5101-3_24 · Cited by 2

Reducing Misinformation: The Role of Confirmation Frames in Fact-Checking Interventions

Aruguete, N., Batista, F., Calvo, E. F., Altube, M. G., Scartascini, C., & Ventura, T. (2023) · doi:10.18235/0005035 · Cited by 2

The 7th Workshop on Online Abuse and Harms (WOAH)

(2023) · doi:10.18653/v1/2023.woah-1 · Cited by 2

U.K. Finfluencers: Exploring Content, Reach, Responsibility and Public StancePreprint

Ghadafi, E., & Andriotis, P. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5576429 · Cited by 2

Sex, Age, and Patient Experience in Cardiologist Reviews

Yang, A., Rodríguez, F., & Woo, J. P. (2024) · JACC Advances · doi:10.1016/j.jacadv.2024.101046 · Cited by 1

PRAGyan - Connecting the Dots in Tweets

Ravi, R., Ginde, G., & Rokne, J. G. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-78548-1_25 · Cited by 2

A Hotel Ranking Method Through Online Reviews Considering the Cultural Background of Reviewers

Shi, J., Du, X., Wu, J., & Liu, Y. (2025) · International Journal of Fuzzy System Applications · doi:10.4018/ijfsa.371752 · Cited by 2

A hierarchical interaction multimodal model for feature fusion based on RoBERTa-Keyword-ViT

Wang, Y., Zhou, Y., Zhong, M., Mei, Y., Fujita, H., & Aljuaid, H. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06764-z · Cited by 2

Sentiment Analysis with Large Language Models Applied to the Federal Reserve Beige Book

Espel, T. J. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-99965-9_11 · Cited by 1

Offensive language in reactions to public figures in polarised discourse online

Kulik, M., Budzyńska, K., Zhang, H., Paquin, M.-A., & Konat, B. (2025) · Journal of Language Aggression and Conflict · doi:10.1075/jlac.00136.kul · Cited by 1

Exploring negative experiences in psychotherapy using an NLP approach on online forum data

Steinbrenner, T., Lalk, C., Kabjesz, A., Ferizaj, D., Loray, J. S. P., Iovoli, F., & Rubel, J. A. (2025) · npj Mental Health Research · doi:10.1038/s44184-025-00172-4 · Cited by 1

Reference Points, Loss Aversion, and Sport: Text Analysis of Football Highlight Comments

Park, E., Ryu, Y., Jang, W., Lee, G., & Pyun, H. (2025) · International Journal of Sport Finance · doi:10.32731/ijsf.202.052025.04 · Cited by 1

Large Language Modeling–Enabled Analysis of Atrial Fibrillation on Social Media

Parsa, S., Somani, S. S., Rogers, A. J., Hernandez‐Boussard, T., Jain, S. S., & Rodríguez, F. (2025) · Journal of the American Heart Association · doi:10.1161/jaha.125.043999 · Cited by 1

Intelligent Sustainability: Evaluating Transformers for Cryptocurrency Environmental Claims

Bouzari, P., Fekete‐Farkas, M., & Szalay, Z. G. (2025) · Information · doi:10.3390/info16121022 · Cited by 1

Unveiling Broader Societal Networks for Megaprojects through Mass-Media Evidence

Zhu, L., Li, Y., & Wang, Y. (2025) · Journal of Construction Engineering and Management · doi:10.1061/jcemd4.coeng-16601 · Cited by 1

Sentiment-Driven Differential Engagement: Hyperpartisan Vs. Non-hyperpartisan Users on X

Mohammadinodooshan, A., & Carlsson, N. F. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-13821-7_10 · Cited by 1

State-wise linear modulation (SLim): A novel approach for steering large language models

hiremath, B., Polignano, M., Levantesi, M., Semeraro, G. M., Luca, E. W. D., & Poznanski, A. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.108708 · Cited by 1

Behind Closed Doors, in Public View: Attitudes Toward Elder Family Financial Exploitation Based on Social Media

Kilaberia, T. R., Zeng, W., & An, R. (2026) · Journal of Family Violence · doi:10.1007/s10896-026-01044-x · Cited by 1

How Consumer Attention Shapes Personalized Experiences in Generative AI Products: A Configurational Perspective

Ye, X., Wang, Y., Wang, C. L., Shafiee, S., & Lee, S. H. (2026) · International Journal of Consumer Studies · doi:10.1111/ijcs.70199 · Cited by 1

CommSense: Facilitating Bias-Aware and Reflective Navigation of Online Comments for Rational Judgment

Ouyang, Y., Gao, S., Wang, R., Zhu, H., Shao, Y., Gu, X., & Li, Q. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3790530 · Cited by 3

Podcast Politics: An NLP Analysis of Parasocial Relationships in Instagram Comments After Candidate Appearances

Hewitt, C., & Booth, H. (2026) · Journal of Radio & Audio Media · doi:10.1080/19376529.2026.2616253 · Cited by 1

Tourist Sentiment in Bali Before and During COVID-19: A Spatial-Temporal and Thematic Analysis Using Twitter Data

Hanifah, R., Schmidt, S., Zorenböhmer, C., Arifi, D., Resch, B., Malleson, N., Houlden, V., & Comber, A. (2026) · Applied Spatial Analysis and Policy · doi:10.1007/s12061-026-09886-0 · Cited by 1

From speeches to resolutions: veto politics and discursive dominance in the UN Security Council

Matsuoka, T., Tsuchiya, T., Tohkairin, T., Otsuka, K., & Sakamoto, T. (2026) · Journal of Economic Policy Reform · doi:10.1080/17487870.2026.2689940 · Cited by 1

Incorporating Sentiment and Temporal Information for Bitcoin Price Prediction

Shen, F. (2022) · BCP Business & Management · doi:10.54691/bcpbm.v34i.3097 · Cited by 2

Using Big Data and Serverless Architecture to Follow the Emotional Response to the COVID-19 Pandemic in Mexico

León-Sandoval, E., Zareei, M., Barbosa-Santillán, L. I., & Falcón-Morales, L. E. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-23821-5_11 · Cited by 1

A Comparative Study of China's Foreign Ministry Spokesperson's Use of Weibo and Twitter

Cheng, Z., & Shaikh, S. (2022) · IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) · doi:10.1109/asonam55673.2022.10068669 · Cited by 1

Efficient Continue Training of Temporal Language Model with Structural Information

Su, Z., Li, J., Zhang, Z., Zhou, Z., & Zhang, M. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.418 · Cited by 5

Online Patient Attitudes Toward Cutaneous Immune-Related Adverse Events Attributed to Nivolumab and Pembrolizumab: Sentiment Analysis

Powers, C. M., Yang, A., Verma, H., Orloff, J. N., Piontkowski, A. J., & Gulati, N. (2024) · JMIR Dermatology · doi:10.2196/53792 · Cited by 1

Community Needs and Assets: A Computational Analysis of Community Conversations

Chowdhury, M. T. A., Sharma, N. K., & KhudaBukhsh, A. R. (2024) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31315 · Cited by 2

Commenting on local politics: An analysis of YouTube video comments for local government videos

Coats, S. (2024) · Research in Corpus Linguistics · doi:10.32714/ricl.13.01.02 · Cited by 3

The challenges of temporal alignment on Twitter during crises

Pramanick, A., Beck, T., Stowe, K., & Gurevych, I. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.195 · Cited by 3

Analyzing the Trends of Responses to COVID-19 Related Tweets from News Stations: An Analysis of Three Countries

Fisher, A., Sharma, R. R., & Mago, V. (2023) · Studies in computational intelligence · doi:10.1007/978-3-031-36938-4_20 · Cited by 1

Fantastic Biases (What are They) and Where to Find ThemPreprint

Barrière, V. (2024) · Qeios · doi:10.32388/ouxt01 · Cited by 1

Intricate Twitternlp Modelling

Gawande, A. A. (2022) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2022.46099

Using Two Losses and Two Datasets Simultaneously to Improve TempoWiC Accuracy

Pirhadi, M. J., Mirzaei, M., & Eetemadi, S. (2022) · doi:10.18653/v1/2022.evonlp-1.3

Using Deep Mixture-of-Experts to Detect Word Meaning Shift for TempoWiC

Chen, Z., Wang, K., Cai, Z., Zheng, J., He, J., Gao, M., & Zhang, J. (2022) · doi:10.18653/v1/2022.evonlp-1.2

Proceedings of the First Workshop on Ever Evolving NLP (EvoNLP)

(2022) · doi:10.18653/v1/2022.evonlp-1

Find Indicative Users for Rumor Detection Using User Credibility and Stance

Zheng, Y., Tan, Z., Wu, D., & Ning, J. (2023) · International Conference on Communications · doi:10.1109/icc45041.2023.10279801

Selective Memory and Perceived Political PolarizationPreprint

Cai, P. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4598389

WIKITIDE: A WIKIPEDIA-BASED TIMESTAMPED DEFINITION Pairs Dataset

Borkakoty, H., Espinosa-Anke, L., & AMPLYFI, U. (2023) · International conference Recent advances in natural language processing · doi:10.26615/978-954-452-092-2_023 · Cited by 1

Non-Compositionality in Sentiment: New Data and Analyses

Dankers, V., & Lucas, C. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.342

Simple Temporal Adaptation to Changing Label Sets: Hashtag Prediction via Dense KNN

Mireshghallah, N., Vogler, N., He, J., Florez, O. U., El-Kishky, A., & Berg-Kirkpatrick, T. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.452 · Cited by 1

Heterogeneous Priors and Information Choices: Evidence from Sell-Side Financial AnalystsPreprint

Buß, K., & Wessel, J. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4719800

Detection of Online Sexism Using Lexical Features and Transformer

Martinez, E. M., Cuadrado, J., Martínez-Santos, J. C., & Puertas, E. (2023) · IEEE Colombian Caribbean Conference (C3) · doi:10.1109/c358072.2023.10436298

Leveraging Social Media Data for Unobtrusive Measurement of Academics’ Well-BeingPreprint

Müller, S. M., & Bittermann, A. (2024) · doi:10.31219/osf.io/957h8

The Rach3 Dataset: Towards Data-Driven Analysis of Piano Performance Rehearsal

Cancino-Chacón, C., & Pilkov, I. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-56435-2_3

Analysis of the feelings in the reviews of patients versus the evaluation of the ease of use, effectiveness, and satisfaction of prescribed medications

Agudelo, C. A., & Zablah, I. (2024) · Bionatura journal : · doi:10.21931/bj/2024.01.01.24

Optimising public health communication: an analysis of type, sentiment and source of COVID-19 tweets (Preprint)Preprint

Parveen, S., Pereira, A. G., Orjuela, N. G., McHugh, P., Surendran, A., Vornhagen, H., & Vellinga, A. (2024) · doi:10.2196/preprints.59687

Do not shut up and do dribble: social media and TV consumption

Pazzona, M., & Spagnolo, N. (2024) · Journal of Population Economics · doi:10.1007/s00148-024-01034-7

Year-Long Digital Phenotyping and Natural Language Processing of Daily Voice Diaries Reveal Affective and Behavioral Signatures of Real-World Life StressPreprint

Bustamante, C. M. V., Coombs, G., Rahimi-Eichi, H., Mair, P., Onnela, J.-P. “., Baker, J. T., & Buckner, R. L. (2024) · doi:10.31219/osf.io/fn3aj

Crowdsourced reviews reveal substantial disparities in public perceptions of parkingPreprint

Li, L., Hu, S., Dinh, L., & Hemphill, L. (2024) · Research Square · doi:10.21203/rs.3.rs-4803079/v1

Continual Learning for Temporal-Sensitive Question Answering

Yang, W., Xu, Y., Li, Y., Wang, K., Huang, B., & Chen, L. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651518

Experiments in Modeling Disagreement

Tahaei, N., & Bergler, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-71602-7_21

Towards a Social Media-based Disease Surveillance System for Early Detection of Influenza-like Illnesses: A Twitter Case Study inWalesPreprint

Drakesmith, M., Antypas, D., Brown, C. J., Camacho-Collados, J., & Song, J. (2024) · medRxiv · doi:10.1101/2024.11.11.24316812

AI-Q: a Framework to Measure the Robustness of Text Classification Models

Mysliwiec, K., Chinea-Ríos, M., Borrego-Obrador, I., & Franco-Salvador, M. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.09.468

Semantic Shift Stability: Auditing Time-Series Performance Degradation of Pre-trained Models via Semantic Shift of Words in Training Corpus

Ishihara, S., Takahashi, H., & Shirai, H. (2024) · Journal of Natural Language Processing · doi:10.5715/jnlp.31.1563

XLM-T: A Multilingual Language Model Toolkit for Twitter.Preprint

Barbieri, F., Espinosa-Anke, L., & Camacho-Collados, J. (2021) · arXiv (Cornell University) · Cited by 31

TiC-LM: A Web-Scale Benchmark for Time-Continual LLM Pretraining

Li, J. J.-P., Armandpour, M., Mirzadeh, S. I., Mehta, S., Shankar, V., Vemulapalli, R., Samy Bengio, A., Tuzel, O., Farajtabar, M., Pouransari, H., et al. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.1551 · Cited by 2

A Language Model for Large, Complex, Time-Varying Systems

Conner, D. B. (2024) · doi:10.13140/rg.2.2.11537.24166

Modeling Diachronic Change in English Scientific Writing over 300+ Years with Transformer-based Language Model Surprisal

Steuer, J., Krielke, M.-P., Fischer, S., Degaetano-Ortlieb, S., Mosbach, M., & Klakow, D. (2024) · Workshop on Building and Using Comparable Corpora · doi:10.63317/3ubzf7o8hspf · Cited by 5

diaNED: Time-Aware Named Entity Disambiguation for Diachronic Corpora

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