Linking Tweets Towards Geo-Localized Policies: COVID-19 Perspective (original) (raw)
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Journal of Computational Social Science
COVID-19 has proven itself to be one of the most important events of the last two centuries. This defining moment in our lives has created wide-ranging discussions in many segments of our societies, both politically and socially. Over time, the pandemic has been associated with many social and political topics, as well as sentiments and emotions. Twitter offers a platform to understand these effects. The primary objective of this study is to capture the awareness and sentiment about COVID-19-related issues and to find how they relate to the number of cases and deaths in a representative region of the United States. The study uses a unique dataset consisting of over 46 million tweets from over 91,000 users in 88 counties of the state of Ohio, a state-of-the-art deep learning model to measure and detect awareness and emotions. The data collected is analyzed using OLS regression and System-GMM dynamic panel. Findings indicate that the pandemic has drastically changed the perception of the Republican party in the society. Individual motivations are strongly influenced by ideological choices and this ultimately affects individual pandemic-related outcomes. The paper contributes to the literature by expanding the knowledge on COVID-19 (i), offering a representative result for the United States by focusing on an "average" state like Ohio (ii), and incorporating the sentiment and emotions into the calculation of awareness (iii).
Journalism and Media
The COVID-19 pandemic disrupted societies all over the world. In an interconnected and digital global society, social media was the platform not only to convey information and recommendations but also to discuss the pandemic and its consequences. Focusing on the phase of stabilization during the first wave of the pandemic in Western countries, this work analyses the conversation around it through tweets in English. For that purpose, the authors have studied who the most active and influential accounts were, identified the most frequent words in the sample, conducted topic modelling, and researched the predominant sentiments. It was observed that the conversation followed two main lines: a more political and controversial one, which can be exemplified by the relevant presence of former US President Donald Trump, and a more informational one, mostly concerning recommendations to fight the virus, represented by the World Health Organization. In general, sentiments were predominantly ne...
Journal of Computational Social Science, 2021
The COVID-19 pandemic has deeply impacted all aspects of social, professional, and financial life, with concerns and responses being readily published in online social media worldwide. This study employs probabilistic text mining techniques for a large-scale, high-resolution, temporal, and geospatial content analysis of Twitter related discussions. Analysis considered 20,230,833 English language original COVID-19-related tweets with global origin retrieved between January 25, 2020 and April 30, 2020. Fine grain topic analysis identified 91 meaningful topics. Most of the topics showed a temporal evolution with local maxima, underlining the short-lived character of discussions in Twitter. When compared to real-world events, temporal popularity curves showed a good correlation with and quick response to real-world triggers. Geospatial analysis of topics showed that approximately 30% of original English language tweets were contributed by USA-based users, while overall more than 60% of the English language tweets were contributed by users from countries with an official language other than English. High-resolution temporal and geospatial analysis of Twitter content shows potential for political, economic, and social monitoring on a global and national level.
2020
Background: Twitter has been used to track trends and disseminate health information during viral epidemics. On January 21, 2020, the CDC activated its Emergency Operations Center and the WHO released its first situation report about Coronavirus disease 2019 (COVID-19), sparking significant media attention. How Twitter content and sentiment has evolved in the early stages of any outbreak, including the COVID-19 epidemic, has not been described. Objective: To quantify and understand early changes in Twitter activity, content, and sentiment about the COVID-19 epidemic. Design: Observational study. Setting: Twitter platform. Participants: All Twitter users who created or sent a message from January 14th to 28th, 2020. Measurements: We extracted tweets matching hashtags related to COVID-19 and measured frequency of keywords related to infection prevention practices, vaccination, and racial prejudice. We performed a sentiment analysis to identify emotional valence and predominant emotions. We conducted topic modeling to identify and explore discussion topics over time. Results: We evaluated 126,049 tweets from 53,196 unique users. The hourly number of COVID-19-related tweets starkly increased from January 21, 2020 onward. Nearly half (49.5%) of all tweets expressed fear and nearly 30% expressed surprise. The frequency of racially charged tweets closely paralleled the number of newly diagnosed cases of COVID-19. The economic and political impact of the COVID-19 was the most commonly discussed topic, while public health risk and prevention were among the least discussed. Conclusion: Tweets with negative sentiment and emotion parallel the incidence of cases for the COVID-19 outbreak. Twitter is a rich medium that can be leveraged to understand public sentiment in real-time and target public health messages based on user interest and emotion. Funding: None.
Tweets Sentiment Analysis During COVID-19 Pandemic
2020 International Conference on Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), 2020
The aim of this paper is to investigate the impact of social distance on people during COVID-19 pandemic using twitter sentiment analysis through a comparison between the kmeans clustering and Mini-Batch k-means clustering approaches. To find the most common frequent words, two datasets have been investigated (WHO and Bahrain ministry of health datasets) to be as data preparation and exploration. Another two datasets (English and Arabic datasets) are used in the clustering of k-means. In this paper, a comparison between k-means and Mini-Batch k-means is performed to find a pattern. The word frequency shows that there are several words related to the pandemic. The sentiment analysis result show that in USA, Australia, Nigeria, Canada, and England, most tweets are neutral. However, the majority of tweets are positive tweets from both Italy and India. In addition, the k-means cluster in the English dataset reveals several cluster trends where COVID-19 pandemic procedures are addressed in cluster 1, and health workers are encouraged in cluster 3.
Understanding the Societal Disruption due to COVID-19 via User Tweets
2021 IEEE International Conference on Smart Computing (SMARTCOMP), 2021
In this paper, we collect data from Twitter and conduct a linguistic analysis of the user tweets to understand the social and economic disruption caused by the COVID-19 pandemic. To better appreciate peoples' opinions and concerns with regards to the socioeconomic conditions of addiction, mental health, unemployment and immigration, we collect data for a period of approximately 3 months in the beginning of the pandemic. We analyze the term and co-occurrence frequencies to identify the most commonly occurring words and bigrams in the discussion for each of the four categories. We then conduct semantic role labeling to determine the action words in each category and then adopt a LSTM-based dependency parsing model to identify the main nouns linked with these action words. We then adopt a seeded topic modeling approach to automatically identify the main topics of discussion in each category. We finally conclude with a sentiment analysis of the tweets in each category to determine the overall sentiment associated with each category. Our fine-grained linguistic study unearths the difficulties experienced by the people (e.g., action verb need associated with nouns such as aid and assistance in the unemployment category). We also observe that the overall sentiment in the tweets is negative, driven by people experiencing the pains of job loss, deportation, and the difficulty in accessing programs and treatments related to addiction. Our analysis highlights the main challenges experienced by the people during the start of the COVID-19 crisis and lays the foundation for recognizing and developing the most pertinent public and social policies so as to minimize peoples' suffering in case of a future pandemic.
Twitter talk on COVID-19: A temporal examination of topics, trends and sentiments (Preprint)
Journal of Medical Internet Research, 2020
With restricted movements and stay-at-home orders due to COVID-19 pandemic, social media platforms like Twitter have become an outlet for users to express their concerns, opinions and feelings about the pandemic. Individuals, health agencies and governments are using Twitter to communicate about COVID-19. This research builds on the emergent stream of studies to examine COVID-19 related English tweets covering a time period from Jan 1, 2020 to May 9, 2020. We perform a temporal assessment and examine variations in the topics and sentiment-scores to uncover key trends. To examine key themes and topics from COVID-19 related English tweets posted by individuals, and to explore the trends and variations in how the COVID-19 related tweets, key topics and associated sentiments changed over a period of time before and after the disease was declared as pandemic. Combining data from two publicly available COVID-19 tweet datasets with our own search, we compiled a dataset of 13.9 million COVI...
Twitter discussions and emotions about COVID-19 pandemic: a machine learning approach
arXiv (Cornell University), 2020
Background: Public response to the COVID-19 pandemic is important to be measured. Twitter data are an important source for the infodemiology study of public response monitoring. Objective: The objective of the study is to examine coronavirus disease (COVID-19) related discussions, concerns, and sentiments that emerged from tweets posted by Twitter users. Methods: We analyze 4 million Twitter messages related to the COVID-19 pandemic using a list of 25 hashtags such as "coronavirus," "COVID-19," "quarantine" from March 1 to April 21 in 2020. We use a machine learning approach, Latent Dirichlet Allocation (LDA), to identify popular unigram, bigrams, salient topics and themes, and sentiments in the collected Tweets. Results: Popular unigrams include "virus," "lockdown," and "quarantine." Popular bigrams include "COVID-19," "stay home," "corona virus," "social distancing," and "new cases." We identify 13 discussion topics and categorize them into five different themes, such as "public health measures to slow the spread of COVID-19," "social stigma associated with COVID-19," "coronavirus news cases and deaths," "COVID-19 in the United States," and "coronavirus cases in the rest of the world. Across all identified topics, the dominant sentiments for the spread of coronavirus are anticipation that measures that can be taken, followed by a mixed feeling of trust, anger, and fear for different topics. The public reveals a significant feeling of fear when they discuss the coronavirus new cases and deaths than other topics. Conclusion: The study shows that Twitter data and machine learning approaches can be leveraged for infodemiology study by studying the evolving public discussions and sentiments during the COVID-19. As the situation evolves rapidly, several topics are consistently dominant on Twitter, such as "the confirmed cases and death rates," "preventive measures," "health authorities and government policies," "COVID-19 stigma," and "negative psychological reactions (e.g., fear)." death. Real-time monitoring and assessment of the Twitter discussion and concerns can be promising for public health emergency responses and planning. Already emerged pandemic fear, stigma, and mental health concerns may continue to influence public trust when there occurs a second wave of COVID-19 or a new surge of the imminent pandemic.
AI-based Automated Extraction of Location-Oriented COVID-19 Sentiments
Computers, Materials & Continua
The coronavirus disease (COVID-19) pandemic has affected the lives of social media users in an unprecedented manner. They are constantly posting their satisfaction or dissatisfaction over the COVID-19 situation at their location of interest. Therefore, understanding location-oriented sentiments about this situation is of prime importance for political leaders, and strategic decision-makers. To this end, we present a new fully automated algorithm based on artificial intelligence (AI), for extraction of location-oriented public sentiments on the COVID-19 situation. We designed the proposed system to obtain exhaustive knowledge and insights on social media feeds related to COVID-19 in 110 languages through AI-based translation, sentiment analysis, location entity detection, and decomposition tree analysis. We deployed fully automated algorithm on live Twitter feed from July 15, 2021 and it is still running as of 12 January, 2022. The system was evaluated on a limited dataset between July 15, 2021 to August 10, 2021. During this evaluation timeframe 150,000 tweets were analyzed and our algorithm found that 9,900 tweets contained one or more location entities. In total, 13,220 location entities were detected during the evaluation period, and the rates of average precision and recall rate were 0.901 and 0.967, respectively. As of 12 January, 2022, the proposed solution has detected 43,169 locations using entity recognition. According to the best of our knowledge, this study is the first to report location intelligence with entity detection, sentiment analysis, and decomposition tree analysis on social media messages related to COVID-19 and has covered the largest set of languages.