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Characterizing Spontaneous Ideation Contest on Social Media: Case Study on the Name Change of Facebook to Meta.

, , , and . IEEE Big Data, page 776-783. IEEE, (2022)

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Confusion Prediction from Eye-Tracking Data: Experiments with Machine Learning., , , , , and . ICIST, page 5:1-5:9. ACM, (2019)The Wisdom of the Few: A Collaborative Filtering Approach Based on Expert Opinions from the Web, , , , and . Proceedings of the 32Nd International ACM SIGIR Conference on Research and Development in Information Retrieval, page 532--539. New York, NY, USA, ACM, (2009)FrameAxis: Characterizing Framing Bias and Intensity with Word Embedding., , and . CoRR, (2020)Tanbih: Get To Know What You Are Reading., , , , , , , , , and 3 other author(s). EMNLP/IJCNLP (3), page 223-228. Association for Computational Linguistics, (2019)Fragile online relationship: a first look at unfollow dynamics in twitter., , and . CHI, page 1091-1100. ACM, (2011)Culturally-Grounded Analysis of Everyday Creativity in Social Media: A Case Study in Qatari Context., , , , , , and . Creativity & Cognition, page 209-221. ACM, (2017)Political Discussions in Homogeneous and Cross-Cutting Communication Spaces., , , and . ICWSM, page 68-79. AAAI Press, (2019)Viewed by too many or viewed too little: Using information dissemination for audience segmentation., , , , and . ASIST, volume 54 of Proc. Assoc. Inf. Sci. Technol., page 189-196. Wiley, (2017)What Was Written vs. Who Read It: News Media Profiling Using Text Analysis and Social Media Context., , , , , , , and . ACL, page 3364-3374. Association for Computational Linguistics, (2020)I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system., , , , and . Internet Measurement Conference, page 1-14. ACM, (2007)