1 |
Kabir, Y., & Madria, S. (2020). CoronaVis: A real-time COVID-19 Tweets data analyzer and data repository. arXiv. https://arxiv.org/abs/2004.13932.
|
2 |
Li, C., Wang, H., Zhang, Z., Sun, A., & Ma, Z. (2016, July 17- 21). Topic modeling for short texts with auxiliary word embeddings. In R. Perego & F. Sebastiani (Eds.), SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval (pp. 165-174). ACM.
|
3 |
Dai, X., Bikdash, M., & Meyer, B. (2017, March 30-April 2). From social media to public health surveillance: Word embedding based clustering method for Twitter classification. In Institute of Electrical and Electronics Engineers (IEEE) (Ed.), SoutheastCon 2017 (pp. 1-7). IEEE.
|
4 |
Stevens, K., Kegelmeyer, P., Andrzejewski, D., & Buttler, D. (2012, July 12-14). Exploring topic coherence over many models and many topics. In J. Tsujii (Ed.), EMNLP-CoNLL '12: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (pp. 952-961). Association for Computational Linguistics.
|
5 |
Weng, J., Lim, E.-P., Jiang, J., & He, Q. (2010, February 3-6). TwitterRank: Finding topic-sensitive influential Twitterers. In B. D. Davison & T. Suel (Eds.), WSDM'10: Third ACM International Conference on Web Search and Data Mining (pp. 261-270). ACM. https://doi.org/10.1145/1718487.1718520.
DOI
|
6 |
Wicke, P., & Bolognesi, M. M. (2020). Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter. arXiv. https://arxiv.org/abs/2004.06986.
|
7 |
World Health Organization (WHO). (2020). Coronavirus disease (COVID-19) situation report - 142. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200610-covid-19-sitrep-142.pdf?sfvrsn=180898cd_6.
|
8 |
Zaharia, M., Xin, R. S., Wendell, P., Das, T., Armbrust, M., Dave, A., Meng, X., Rosen, J., Venkataraman, S., Franklin, M. J., Ghodsi, A., Gonzalez, J., Shenker, S., & Stoica, I. (2016). Apache Spark: A unified engine for big data processing. Communications of the ACM, 59(11), 56-65. https://doi.org/10.1145/2934664.
DOI
|
9 |
Sharma, K., Seo, S., Meng, C., Rambhatla, S., Dua, A., & Liu, Y. (2020). COVID-19 on social media: Analyzing misinformation in Twitter conversations. arXiv. https://arxiv.org/abs/2003.12309.
|
10 |
Liu, C., Liu, Z., Li, T., & Xia, B. (2018, July 1-3). Topic modeling for noisy short texts with multiple relations. In X. He (Ed.), Proceedings of the 30th International Conference on Software Engineering and Knowledge Engineering (pp. 610-609). KSI Research Inc. and Knowledge Systems Institute Graduate School. http://ksiresearchorg.ipage.com/seke/seke18.html
|
11 |
Lopez, C. E., Vasu, M., & Gallemore, C. (2020). Understanding the perception of COVID-19 policies by mining a multilanguage Twitter dataset. arXiv. https://arxiv.org/abs/2003.10359.
|
12 |
Medford, R. J., Saleh, S. N., Sumarsono, A., Perl, T. M., & Lehmann, C. U. (2020). An "infodemic": Leveraging high-volume Twitter data to understand early public sentiment for the coronavirus disease 2019 outbreak. Open Forum Infectious Diseases, 7(7), ofaa258. https://doi.org/10.1093/ofid/ofaa258.
DOI
|
13 |
Nikita, M. (2015). Select number of topics for LDA model. http://rstudio-pubs-static.s3.amazonaws.com/107657_4cdc6f600fe44cc8b2600f6f9c470ce8.html.
|
14 |
Mehrotra, R., Sanner, S., Buntine, W., & Xie, L. (2013, July 28-August 1). Improving LDA topic models for microblogs via Tweet pooling and automatic labeling. In G. J. F. Jones & P. Sheridan (Eds.), SIGIR '13: The 36th International ACM SIGIR conference on research and development in information retrieval (pp. 889-892). ACM. https://doi.org/10.1145/2484028.2484166
DOI
|
15 |
Mendoza, M., Poblete, B., & Valderrama, I. (2019). Nowcasting earthquake damages with Twitter. EPJ Data Science, 8, 3. https://doi.org/10.1140/epjds/s13688-019-0181-0.
DOI
|
16 |
Newman, D., Lau, J. H., Grieser, K., & Baldwin, T. (2010, June 2-4). Automatic evaluation of topic coherence. In R. M. Kaplan (Ed.), HLT '10: Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics (pp. 100-108). Association for Computational Linguistics.
|
17 |
Cinelli, M., Quattrociocchi, W., Galeazzi, A., Valensise, C. M., Brugnoli, E., Schmidt, A. L., Zola, P., Zollo, F., & Scala, A. (2020). The COVID-19 social media infodemic. arXiv. https://arxiv.org/abs/2003.05004.
|
18 |
Zhao, W., Chen, J. J., Perkins, R., Liu, Z., Ge, W., Ding, Y., & Zou, W. (2015). A heuristic approach to determine an appropriate number of topics in topic modeling. BMC Bioinformatics, 16(Supplement 13), S8. https://doi.org/10.1186/1471-2105-16-S13-S8.
DOI
|
19 |
Zhao, W. X., Jiang, J., Weng, J., He, J., Lim, E.-P., Yan, H., & Li, X. (2011, April 18-21). Comparing Twitter and traditional media using topic models. In P. Clough, C. Foley, C. Gurrin, G. J. F. Jones, W. Kraaij, H. Lee, & V. Mudoch (Eds.), 33rd European Conference on Information Retrieval Research, ECIR 2011 (pp. 338-349). Springer.
|
20 |
Rehurek, R., & Sojka, P. (2011). Gensim-statistical semantics in Python. https://www.fi.muni.cz/usr/sojka/posters/rehurek-sojka-scipy2011.pdf.
|
21 |
Maier, D., Waldherr, A., Miltner, P., Wiedemann, G., Niekler, A., Keinert, A., Pfetsch, B., Heyer, G., Reber, U., Haussler, T., Schmid-Petri, H., & Adam, S. (2018). Applying LDA topic modeling in communication research: Toward a valid and reliable methodology. Communication Methods and Measures, 12(2-3), 93-118. https://doi.org/10.1080/19312458.2018.1430754.
DOI
|
22 |
Pourebrahim, N., Sultana, S., Edwards, J., Gochanour, A., & Mohanty, S. (2019). Understanding communication dynamics on Twitter during natural disasters: A case study of Hurricane Sandy. International Journal of Disaster Risk Reduction, 37, 101176. https://doi.org/10.1016/j.ijdrr.2019.101176.
DOI
|
23 |
Mimno, D., Wallach, H. M., Talley, E., Leenders, M., & McCallum, A. (2011, July 27-31). Optimizing semantic coherence in topic models. In P. Merlo (Ed.), EMNLP '11: Proceedings of the Conference on Empirical Methods in Natural Language Processing (pp. 262-272). Association for Computational Linguistics.
|
24 |
Ordun, C., Purushotham, S., & Raff, E. (2020). Exploratory analysis of Covid-19 Tweets using topic modeling, UMAP, and DiGraphs. arXiv. https://arxiv.org/abs/2005.03082.
|
25 |
Owoputi, O., O'Connor, B., Dyer, C., Gimpel, K., Schneider, N., & Smith, N. A. (2013, June 9-14). Improved part-of-speech tagging for online conversational text with word clusters. Proceedings of NAACL-HLT 2013 (pp. 380-390). Association for Computational Linguistics. https://www.aclweb.org/anthology/N13-1039.pdf
|
26 |
Ponweiser, M. (2012). Latent Dirichlet allocation in R. [Diploma thesis]. Vienna University, Vienna, Austria. https://epub.wu.ac.at/3558/
|
27 |
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. The Journal of Machine Learning Research, 3, 993-1022. https://doi.org/10.1145/2615569.2615680.
DOI
|
28 |
Bruns, A., & Hanusch, F. (2017). Conflict imagery in a connective environment: Audiovisual content on Twitter following the 2015/2016 terror attacks in Paris and Brussels. Media, Culture & Society, 39(8), 1122-1141. https://doi.org/10.1177%2F0163443717725574.
DOI
|
29 |
Alvarez-Melis, D., & Saveski, M. (2016, May 17-20). Topic modeling in Twitter: Aggregating tweets by conversations. Paper presented at the 10th International AAAI Conference on Web and Social Media, Cologne, Germany.
|
30 |
Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., Freeman, J., Tsai, D., Amde, M., Owen, S., Xin, D., Xin, R., Franklin, M. J., Zadeh, R., Zaharia, M., & Talwalkar, A. (2016). MLlib: Machine learning in Apache Spark. Journal of Machine Learning Research, 17(2016), 1-7. https://www.jmlr.org/papers/volume17/15-237/15-237.pdf.
|
31 |
Rossetti, M., Stella, F., & Zanker, M. (2016). Analyzing user reviews in tourism with topic models. Information Technology & Tourism, 16(1), 5-21. https://doi.org/10.1007/s40558-015-0035-y.
DOI
|
32 |
Sha, H., Hasan, M. A., Mohler, G., & Brantingham, P. J. (2020). Dynamic topic modeling of the COVID-19 Twitter narrative among U.S. governors and cabinet executives. arXiv. https://arxiv.org/abs/2004.11692.
|
33 |
Sharma, C., & Bedi, P. (2018, October 10-12). Mitigating popularity bias in Twitter- recommending novel hashtags using pooled tweets. In S. K. Niranjan (Ed.), Proceedings of the 3rd International Conference on Contemporary Computing and Informatics (iC3 I - 2018) (pp. 166-171). IEEE. https://doi.org/10.1109/IC3I44769.2018.9007248
DOI
|
34 |
Chen, L., Lyu, H., Yang, T., Wang, Y., & Luo, J. (2020). In the eyes of the beholder: Analyzing social media use of neutral and controversial terms for COVID-19. arXiv. https://arxiv.org/abs/2004.10225.
|
35 |
Singh, L., Bansal, S., Bode, L., Budak, C., Chi, G., Kawintiranon, K., Padden, C., Vanarsdall, R., Vraga, E., & Wang, Y. (2020). A first look at COVID-19 information and misinformation sharing on Twitter. arXiv. https://arxiv.org/abs/2003.13907.
|
36 |
Abd-Alrazaq, A., Alhuwail, D., Househ, M., Hamdi, M., & Shah, Z. (2020). Top concerns of Tweeters during the COVID-19 pandemic: Infoveillance study. Journal of Medical Internet Research, 22(4), e19016. https://doi.org/10.2196/19016.
DOI
|
37 |
Alam, F., Ofli, F., Imran, M., & Aupetit, M. (2018). A Twitter tale of three hurricanes: Harvey, Irma, and Maria. arXiv. https://arxiv.org/abs/1805.05144.
|
38 |
Aletras, N., & Stevenson, M. (2013, March 19-22). Evaluating topic coherence using distributional semantics. In A. Koller & K. Erk (Eds.), Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) - Long Papers (pp. 13-22). Association for Computational Linguistics.
|
39 |
Aletras, N., & Stevenson, M. (2014, June 22-27). Labelling topics using unsupervised graph-based methods. In K. Toutanova & H. Wu (Eds.), Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 631-636). Association for Computational Linguistics.
|
40 |
Byrd, K., Mansurov, A., & Baysal, O. (2016, May 14-15). Mining Twitter data for influenza detection and surveillance. In P. Kellenberger (Ed.), 2016 IEEE/ACM International Workshop on Software Engineering in Healthcare Systems (SEHS) (pp. 43-49). Institute of Electrical and Electronics Engineers.
|
41 |
Idzelis, M. (2005). Jazzy: The Java open source spell checker. http://jazzy.sourceforge.net.
|
42 |
Smith, A., Lee, T. Y., Poursabzi-Sangdeh, F., Boyd-Graber, J., Elmqvist, N., & Findlater, L. (2017). Evaluating visual representations for topic understanding and their effects on manually generated topic labels. Transactions of the Association for Computational Linguistics, 5, 1-16. https://doi.org/10.1162/tacl_a_00042.
DOI
|
43 |
Banda, J. M., Tekumalla, R., Wang, G., Yu, J., Liu, T., Ding, Y., Artemova, K., Tutubalina, E., & Chowell, G. (2020). A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration. arXiv. https://arxiv.org/abs/2004.03688.
|
44 |
Blei, D. M., & Lafferty, J. D. (2005, December 5-10). Correlated topic models. In Y. Weiss, B. Scholkopf, & J. Platt (Eds.), Advances in Neural Information Processing Systems 18 (NIPS 2005) (pp. 147-154). MIT Press.
|
45 |
Duong, V., Pham, P., Yang, T., Wang, Y., & Luo, J. (2020). The ivory tower lost: How college students respond differently than the general public to the COVID-19 pandemic. arXiv. https://arxiv.org/abs/2004.09968.
|
46 |
Gimpel, K., Schneider, N., & O'Connor, B. (2013). Annotation guidelines for Twitter part-of-speech tagging version 0.3 (March 2013). http://www.ark.cs.cmu.edu/TweetNLP/annot_guidelines.pdf.
|
47 |
Hong, L., & Davison, B. D. (2010, July 25). Empirical study of topic modeling in Twitter. In P. Melville, J. Leskovec, & F. Provost (Eds.), Proceedings of the first workshop on social media analytics (pp. 80-88). ACM. https://doi.org/10.1145/1964858.1964870
DOI
|
48 |
Hutto, C. J., & Gilbert, E. (2014). Vader: A parsimonious rule-based model for sentiment analysis of social media text. Paper presented at 8th International AAAI Conference on Weblogs and Social Media, Ann Arbor, Michigan, USA.
|
49 |
Jordan, S. E., Hovet, S. E., Fung, I. C.-H., Liang, H., Fu, K.-W., & Tse, Z. T. H. (2019). Using Twitter for public health surveillance from monitoring and prediction to public response. Data, 4(1), 6. https://doi.org/10.3390/data4010006.
DOI
|