ABSTRACT
The Twitter has become a powerful medium for people to express their feelings and reactions towards some recent event or happening event. Analyzing and exploring such tweet data gives us in-depth understanding of the underlying event from people perspectives. In this work, we use the power of visualization to explore people’s tweets regarding the COVID-19 pandemic from the time period between April 2020 and March 2021. We use a number of visualizations targeting to show the sentimental polarity of people’s tweets over time and important keywords in people’s tweets and their evolution over time. The resulting visualizations give us an opportunity to explore people’s feelings and reactions during a whole pandemic year and to see how it evolved over time.
- William B. Claster, Malcolm Cooper, and Philip Sallis. 2010. Thailand – Tourism and Conflict: Modeling Sentiment from Twitter Tweets Using Naïve Bayes and Unsupervised Artificial Neural Nets. In 2010 Second International Conference on Computational Intelligence, Modelling and Simulation. 89–94. https://doi.org/10.1109/CIMSiM.2010.98Google ScholarDigital Library
- Marian Dörk, Daniel Gruen, Carey Williamson, and Sheelagh Carpendale. 2010. A Visual Backchannel for Large-Scale Events. IEEE Transactions on Visualization and Computer Graphics 16, 6(2010), 1129–1138. https://doi.org/10.1109/TVCG.2010.129Google ScholarDigital Library
- Alex Godwin, Yongxin Wang, and John T. Stasko. 2017. TypoTweet Maps: Characterizing Urban Areas through Typographic Social Media Visualization. In Proceedings of the Eurographics/IEEE VGTC Conference on Visualization: Short Papers (Barcelona, Spain) (EuroVis ’17). Eurographics Association, Goslar, DEU, 25–29. https://doi.org/10.2312/eurovisshort.20171128Google ScholarDigital Library
- Orland Hoeber, Larena Hoeber, Maha El Meseery, Kenneth Odoh, and Radhika Gopi. 2016. Visual Twitter Analytics (Vista): Temporally changing sentiment and the discovery of emergent themes within sport event tweets. Online Inf. Rev. 40(2016), 25–41.Google ScholarCross Ref
- Shah Rukh Humayoun, Saman Ardalan, Ragaad AlTarawneh, and Achim Ebert. 2017. TExVis: An Interactive Visual Tool to Explore Twitter Data. In EuroVis 2017 - Short Papers, Barbora Kozlikova, Tobias Schreck, and Thomas Wischgoll (Eds.). The Eurographics Association. https://doi.org/10.2312/eurovisshort.20171149Google ScholarDigital Library
- Shah Rukh Humayoun, Ibrahim Mansour, and Ragaad AlTarawneh. 2021. TEVisE: An Interactive Visual Analytics Tool to Explore Evolution of Keywords’ Relations in Tweet Data. In Human-Computer Interaction – INTERACT 2021, Carmelo Ardito, Rosa Lanzilotti, Alessio Malizia, Helen Petrie, Antonio Piccinno, Giuseppe Desolda, and Kori Inkpen (Eds.). Springer International Publishing, Cham, 579–599.Google Scholar
- Renato Kempter, Valentina Sintsova, Claudiu Cristian Musat, and Pearl Pu. 2014. EmotionWatch: Visualizing Fine-Grained Emotions in Event-Related Tweets. In ICWSM.Google Scholar
- Thomas Kraft, Derek X. Wang, Jeffrey Delawder, Wenwen Dou, Li Yu, and William Ribarsky. 2013. Less After-the-Fact: Investigative visual analysis of events from streaming twitter. In 2013 IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV). 95–103. https://doi.org/10.1109/LDAV.2013.6675163Google ScholarCross Ref
- Bongshin Lee, Amy Karlson, and Nathalie Henry Riche. 2010. SparkClouds: Visualizing Trends in Tag Clouds. In In IEEE TVCG (Proceedings of Infovis 2010)(in ieee tvcg (proceedings of infovis 2010) ed.). IEEE. https://www.microsoft.com/en-us/research/publication/sparkclouds-visualizing-trends-in-tag-clouds/Google Scholar
- Jie Li, Siming Chen, Gennady Andrienko, and Natalia Andrienko. 2018. Visual Exploration of Spatial and Temporal Variations of Tweet Topic Popularity. In EuroVis Workshop on Visual Analytics (EuroVA), Christian Tominski and Tatiana von Landesberger (Eds.). The Eurographics Association. https://doi.org/10.2312/eurova.20181105Google Scholar
- Shenghua Liu, Wenjun Zhu, Ning Xu, Fangtao Li, Xue-qi Cheng, Yue Liu, and Yuanzhuo Wang. 2013. Co-Training and Visualizing Sentiment Evolvement for Tweet Events. In Proceedings of the 22nd International Conference on World Wide Web (Rio de Janeiro, Brazil) (WWW ’13 Companion). Association for Computing Machinery, New York, NY, USA, 105–106. https://doi.org/10.1145/2487788.2487836Google ScholarDigital Library
- Yafeng Lu, Xia Hu, Feng Wang, Shamanth Kumar, Huan Liu, and Ross Maciejewski. 2015. Visualizing Social Media Sentiment in Disaster Scenarios. In Proceedings of the 24th International Conference on World Wide Web (Florence, Italy) (WWW ’15 Companion). Association for Computing Machinery, New York, NY, USA, 1211–1215. https://doi.org/10.1145/2740908.2741720Google ScholarDigital Library
- Alan M. MacEachren, Anuj Jaiswal, Anthony C. Robinson, Scott Pezanowski, Alexander Savelyev, Prasenjit Mitra, Xiao Zhang, and Justine Blanford. 2011. SensePlace2: GeoTwitter analytics support for situational awareness. In 2011 IEEE Conference on Visual Analytics Science and Technology (VAST). 181–190. https://doi.org/10.1109/VAST.2011.6102456Google ScholarCross Ref
- Sana Malik, Alison Smith, Timothy Hawes, Panagis Papadatos, Jianyu Li, Cody Dunne, and Ben Shneiderman. 2013. TopicFlow: Visualizing topic alignment of Twitter data over time. In 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013). 720–726. https://doi.org/10.1145/2492517.2492639Google ScholarDigital Library
- Myriam Munezero, Calkin Suero Montero, Maxim Mozgovoy, and Erkki Sutinen. 2015. EmoTwitter – A Fine-Grained Visualization System for Identifying Enduring Sentiments in Tweets. In Computational Linguistics and Intelligent Text Processing, Alexander Gelbukh (Ed.). Springer International Publishing, Cham, 78–91.Google Scholar
- Vu Dung Nguyen, Blesson Varghese, and Adam Barker. 2013. The Royal Birth of 2013: Analysing and Visualising Public Sentiment in the UK Using Twitter. CoRR abs/1308.1847(2013). arXiv:1308.1847http://arxiv.org/abs/1308.1847Google Scholar
- Dennis Thom, Harald Bosch, Steffen Koch, Michael Wörner, and Thomas Ertl. 2012. Spatiotemporal anomaly detection through visual analysis of geolocated Twitter messages. In 2012 IEEE Pacific Visualization Symposium. 41–48. https://doi.org/10.1109/PacificVis.2012.6183572Google ScholarDigital Library
- Dennis Thom, Robert Krüger, Thomas Ertl, Ulrike Bechstedt, Axel Platz, Julia Zisgen, and Bernd Volland. 2015. Can twitter really save your life? A case study of visual social media analytics for situation awareness. In 2015 IEEE Pacific Visualization Symposium (PacificVis). 183–190. https://doi.org/10.1109/PACIFICVIS.2015.7156376Google ScholarCross Ref
- Franz Wanner, Andreas Weiler, and Tobias Schreck. 2012. Topic Tracker: Shape-based Visualization for Trend and Sentiment Tracking in Twitter.Google Scholar
- Jichang Zhao, Li Dong, Junjie Wu, and Ke Xu. 2012. MoodLens: an emoticon-based sentiment analysis system for chinese tweets. In KDD.Google Scholar
Recommendations
Wording Matters: the Effect of Linguistic Characteristics and Political Ideology on Resharing of COVID-19 Vaccine Tweets
Social media platforms are frequently used to share information and opinions around vaccinations. The more often a message is reshared, the wider the reach of the message and potential influence it may have on shaping people’s opinions to get vaccinated ...
TEVisE: An Interactive Visual Analytics Tool to Explore Evolution of Keywords’ Relations in Tweet Data
Human-Computer Interaction – INTERACT 2021AbstractRecently, a new window to explore tweet data has been opened in TExVis tool through visualizing the relations between the frequent keywords. However, timeline exploration of tweet data, not present in TExVis, could play a critical factor in ...
Are Mutated Misinformation More Contagious? A Case Study of COVID-19 Misinformation on Twitter
WebSci '22: Proceedings of the 14th ACM Web Science Conference 2022The spread of online misinformation has become a major global risk. Understanding how misinformation propagates on social media is vital. While prior studies suggest that the content factors, such as emotion and topic in texts, are closely related to the ...
Comments