Identifying and Characterizing the Propagation Scale of COVID-19 Situational Information on Twitter: A Hybrid Text Analytic Approach

During the recent pandemic of COVID-19, an increasing amount of information has been propagated on social media. This situational information is valuable for public authorities. Therefore, this study characterized the propagation scale of situational information types by harnessing the power of natural language processing techniques and machine learning algorithms. We observed that the length of the post has a positive correlation with type 1 information (announcements), and negative words were mostly used in type 5 information (criticizing the government), whereas anxiety-related words have a negative effect on the amount of retweeted type 0 (precautions) and type 2 (donations) information. This type of research study not only contributes to the situational information literature by comprehensively defining categories but also provides data-oriented practical insights into information so that management authorities can formulate response strategies after the pandemic. Our approach is one of its kind and combines Twitter content features, user features and LIWC linguistic features with machine learning algorithms to analyze the propagation scale of situational information, and it achieved 77% accuracy with SVM while classifying the information categories.

[1]  Santanu Kumar Rath,et al.  Classification of sentiment reviews using n-gram machine learning approach , 2016, Expert Syst. Appl..

[2]  Gonzalo A. Ruz,et al.  Sentiment analysis of Twitter data during critical events through Bayesian networks classifiers , 2020, Future Gener. Comput. Syst..

[3]  Juan M. Corchado,et al.  A polarity analysis framework for Twitter messages , 2015, Appl. Math. Comput..

[4]  Muhammad Shahid,et al.  Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques , 2016, J. King Saud Univ. Comput. Inf. Sci..

[5]  Yogesh Kumar Dwivedi,et al.  A deep multi-modal neural network for informative Twitter content classification during emergencies , 2020, Ann. Oper. Res..

[6]  Shahzad Qaiser,et al.  Text Mining: Use of TF-IDF to Examine the Relevance of Words to Documents , 2018, International Journal of Computer Applications.

[7]  Alfonso J. Pedraza-Martinez,et al.  Social Media for Disaster Management: Operational Value of the Social Conversation , 2019, Production and Operations Management.

[8]  Gerardo Chowell,et al.  A large-scale COVID-19 Twitter chatter dataset for open scientific research - an international collaboration , 2020, ArXiv.

[9]  Muhammad Imran,et al.  Automatic identification of eyewitness messages on twitter during disasters , 2020, Inf. Process. Manag..

[10]  Jun Tian,et al.  Characterizing information propagation patterns in emergencies: A case study with Yiliang Earthquake , 2018, Int. J. Inf. Manag..

[11]  Catherine M. Vera-Burgos,et al.  Using Twitter for crisis communications in a natural disaster: Hurricane Harvey , 2020, Heliyon.

[12]  Özgür Kisi,et al.  Precipitation forecasting by using wavelet-support vector machine conjunction model , 2012, Eng. Appl. Artif. Intell..

[13]  J. Pennebaker,et al.  The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods , 2010 .

[14]  Xin Wang,et al.  Detecting and Analyzing Influenza Epidemics with Social Media in China , 2014, PAKDD.

[15]  G. Leung,et al.  Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study , 2020, The Lancet.

[16]  María Martínez-Rojas,et al.  Twitter as a tool for the management and analysis of emergency situations: A systematic literature review , 2018, Int. J. Inf. Manag..

[17]  Tao Wang,et al.  Characterizing the Propagation of Situational Information in Social Media During COVID-19 Epidemic: A Case Study on Weibo , 2020, IEEE Transactions on Computational Social Systems.

[18]  Ireneus Kagashe,et al.  Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data , 2017, Journal of medical Internet research.

[19]  Edson C. Tandoc,et al.  Communicating on Twitter during a disaster: An analysis of tweets during Typhoon Haiyan in the Philippines , 2015, Comput. Hum. Behav..

[20]  Türkay Dereli,et al.  Comparison of different machine learning techniques on location extraction by utilizing geo-tagged tweets: A case study , 2020, Adv. Eng. Informatics.

[21]  Michael J. Stern,et al.  Digital Inequality and Place: The Effects of Technological Diffusion on Internet Proficiency and Usage across Rural, Suburban, and Urban Counties , 2009 .

[22]  Lamjed Ben Said,et al.  Hybrid System for Information Extraction from Social Media Text: Drug Abuse Case Study , 2019, KES.

[23]  Kristina Lerman,et al.  Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set , 2020, JMIR public health and surveillance.

[24]  Pooya Moradian Zadeh,et al.  Monitoring the Dynamics of Emotions during COVID-19 Using Twitter Data , 2020, EUSPN/ICTH.

[25]  Nirmalya Thakur,et al.  An Ambient Intelligence-Based Human Behavior Monitoring Framework for Ubiquitous Environments , 2021, Inf..

[26]  Shahbaz Syed,et al.  The Twitter pandemic: The critical role of Twitter in the dissemination of medical information and misinformation during the COVID-19 pandemic , 2020, CJEM.

[27]  Alok N. Choudhary,et al.  Forecasting Influenza Levels Using Real-Time Social Media Streams , 2017, 2017 IEEE International Conference on Healthcare Informatics (ICHI).

[28]  Rong Jin,et al.  Understanding bag-of-words model: a statistical framework , 2010, Int. J. Mach. Learn. Cybern..

[29]  Markus Bayer,et al.  Rapid relevance classification of social media posts in disasters and emergencies: A system and evaluation featuring active, incremental and online learning , 2020, Inf. Process. Manag..

[30]  Cheng Zhang,et al.  Crowd or Hubs: information diffusion patterns in online social networks in disasters , 2020 .

[31]  Ed H. Chi,et al.  Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network , 2010, 2010 IEEE Second International Conference on Social Computing.

[32]  Hong Wen,et al.  Effect of anger, anxiety, and sadness on the propagation scale of social media posts after natural disasters , 2020, Inf. Process. Manag..

[33]  Loni Hagen,et al.  Social Media Use for Crisis and Emergency Risk Communications During the Zika Health Crisis , 2020, Digit. Gov. Res. Pract..

[34]  Vincent A. Knight,et al.  Tweeting the terror: modelling the social media reaction to the Woolwich terrorist attack , 2014, Social Network Analysis and Mining.

[35]  Yisheng Lv,et al.  A hybrid learning method for the data-driven design of linguistic dynamic systems , 2019, IEEE/CAA Journal of Automatica Sinica.

[36]  Jin Mao,et al.  Uncovering sentiment and retweet patterns of disaster-related tweets from a spatiotemporal perspective - A case study of Hurricane Harvey , 2020, Telematics Informatics.

[37]  José Ramón Cano,et al.  CommuniMents: A Framework for Detecting Community Based Sentiments for Events , 2017, Int. J. Semantic Web Inf. Syst..

[38]  Leysia Palen,et al.  Microblogging during two natural hazards events: what twitter may contribute to situational awareness , 2010, CHI.

[39]  Matthias Hofer,et al.  Perceived bridging and bonding social capital on Twitter: Differentiating between followers and followees , 2013, Comput. Hum. Behav..

[40]  Derek Ruths,et al.  Novel Situational Information in Mass Emergencies: What does Twitter Provide? , 2014 .

[41]  Qingpeng Zhang,et al.  Information Diffusion on Social Media During Natural Disasters , 2018, IEEE Transactions on Computational Social Systems.

[42]  Sreenivasulu Madichetty,et al.  A Neural-Based Approach for Detecting the Situational Information From Twitter During Disaster , 2021, IEEE Transactions on Computational Social Systems.

[43]  N. Bhuvana,et al.  Facebook and Whatsapp as disaster management tools during the Chennai (India) floods of 2015 , 2019, International Journal of Disaster Risk Reduction.

[44]  Andrei Romascanu,et al.  Using deep learning and social network analysis to understand and manage extreme flooding , 2020 .

[45]  Niloy Ganguly,et al.  Extracting Situational Information from Microblogs during Disaster Events: a Classification-Summarization Approach , 2015, CIKM.

[46]  Ali Mostafavi,et al.  Spatial biases in crowdsourced data: Social media content attention concentrates on populous areas in disasters , 2020, Comput. Environ. Urban Syst..

[47]  Gouda I. Salama,et al.  A Novel Approach for Ontology-Based Feature Vector Generation for Web Text Document Classification , 2018, Int. J. Softw. Innov..

[48]  Gyu Sang Choi,et al.  COVID-19 Future Forecasting Using Supervised Machine Learning Models , 2020, IEEE Access.

[49]  Jemal H. Abawajy,et al.  Tweetluenza: Predicting flu trends from twitter data , 2019, Big Data Min. Anal..

[50]  Mohammad Aman Ullah,et al.  An algorithm and method for sentiment analysis using the text and emoticon , 2020, ICT Express.

[51]  Amir Karami,et al.  Twitter speaks: A case of national disaster situational awareness , 2019, J. Inf. Sci..

[52]  Klaifer Garcia,et al.  Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA , 2020, Applied Soft Computing.

[53]  Hassan Mostafa,et al.  Breast Cancer Diagnosis Using Image Processing and Machine Learning for Elastography Images , 2019, 2019 8th International Conference on Modern Circuits and Systems Technologies (MOCAST).