Blogs and social networks have recently become a valuable resource for mining sentiments in fields as diverse as customer relationship management, public opinion tracking and text filtering. In fact knowledge obtained from social networks such as Twitter and Facebook has been shown to be extremely valuable to marketing research companies, public opinion organizations and other text mining entities. However, Web texts have been classified as noisy as they represent considerable problems both at the lexical and the syntactic levels. In this research we used a random sample of 3516 tweets to evaluate consumers' sentiment towards well-known brands such as Nokia, T-Mobile, IBM, KLM and DHL. We used an expert-predefined lexicon including around 6800 seed adjectives with known orientation to conduct the analysis. Our results indicate a generally positive consumer sentiment towards several famous brands. By using both a qualitative and quantitative methodology to analyze brands' tweets, this study adds breadth and depth to the debate over attitudes towards cosmopolitan brands.

More than words: Social networks' text mining for consumer brand sentiments

Review of The people's choice: How the voter makes up his mind in a presidential campaign.

Background: At the time of this writing, the coronavirus disease (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources, and economies around the world. Social distancing measures, travel bans, self-quarantines, and business closures are changing the very fabric of societies worldwide. With people forced out of public spaces, much of the conversation about these phenomena now occurs online on social media platforms like Twitter.
Objective: In this paper, we describe a multilingual COVID-19 Twitter data set that we are making available to the research community via our COVID-19-TweetIDs GitHub repository.
Methods: We started this ongoing data collection on January 28, 2020, leveraging Twitter’s streaming application programming interface (API) and Tweepy to follow certain keywords and accounts that were trending at the time data collection began. We used Twitter’s search API to query for past tweets, resulting in the earliest tweets in our collection dating back to January 21, 2020.
Results: Since the inception of our collection, we have actively maintained and updated our GitHub repository on a weekly basis. We have published over 123 million tweets, with over 60% of the tweets in English. This paper also presents basic statistics that show that Twitter activity responds and reacts to COVID-19-related events.
Conclusions: It is our hope that our contribution will enable the study of online conversation dynamics in the context of a planetary-scale epidemic outbreak of unprecedented proportions and implications. This data set could also help track COVID-19-related misinformation and unverified rumors or enable the understanding of fear and panic—and undoubtedly more.

/pdf/tracking-social-media-discourse-about-the-covid-19-pandemic-38av8f8iiz.pdf

Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set.

With the onset of the COVID-19 pandemic, social media has rapidly become a crucial communication tool for information generation, dissemination, and consumption. In this scoping review, we selected and examined peer-reviewed empirical studies relating to COVID-19 and social media during the first outbreak from November, 2019, to November, 2020. From an analysis of 81 studies, we identified five overarching public health themes concerning the role of online social media platforms and COVID-19. These themes focused on: surveying public attitudes, identifying infodemics, assessing mental health, detecting or predicting COVID-19 cases, analysing government responses to the pandemic, and evaluating quality of health information in prevention education videos. Furthermore, our Review emphasises the paucity of studies on the application of machine learning on data from COVID-19-related social media and a scarcity of studies documenting real-time surveillance that was developed with data from social media on COVID-19. For COVID-19, social media can have a crucial role in disseminating health information and tackling infodemics and misinformation.

What social media told us in the time of COVID-19: a scoping review.

At the time of this writing, the novel coronavirus (COVID-19) pandemic outbreak has already put tremendous strain on many countries' citizens, resources and economies around the world. Social distancing measures, travel bans, self-quarantines, and business closures are changing the very fabric of societies worldwide. With people forced out of public spaces, much conversation about these phenomena now occurs online, e.g., on social media platforms like Twitter. In this paper, we describe a multilingual coronavirus (COVID-19) Twitter dataset that we have been continuously collecting since January 22, 2020. We are making our dataset available to the research community (this https URL). It is our hope that our contribution will enable the study of online conversation dynamics in the context of a planetary-scale epidemic outbreak of unprecedented proportions and implications. This dataset could also help track scientific coronavirus misinformation and unverified rumors, or enable the understanding of fear and panic -- and undoubtedly more. Ultimately, this dataset may contribute towards enabling informed solutions and prescribing targeted policy interventions to fight this global crisis.

/pdf/tracking-social-media-discourse-about-the-covid-19-pandemic-2tizrls2yy.pdf

Tracking Social Media Discourse About the COVID-19 Pandemic: Development of a Public Coronavirus Twitter Data Set

Background: SARS-CoV-2 (severe acute respiratory coronavirus 2) was spreading rapidly in South Korea at the end of February 2020 following its initial outbreak in China, making Korea the new center of global attention. The role of social media amid the current coronavirus disease (COVID-19) pandemic has often been criticized, but little systematic research has been conducted on this issue. Social media functions as a convenient source of information in pandemic situations.
Objective: Few infodemiology studies have applied network analysis in conjunction with content analysis. This study investigates information transmission networks and news-sharing behaviors regarding COVID-19 on Twitter in Korea. The real time aggregation of social media data can serve as a starting point for designing strategic messages for health campaigns and establishing an effective communication system during this outbreak.
Methods: Korean COVID-19-related Twitter data were collected on February 29, 2020. Our final sample comprised of 43,832 users and 78,233 relationships on Twitter. We generated four networks in terms of key issues regarding COVID-19 in Korea. This study comparatively investigates how COVID-19-related issues have circulated on Twitter through network analysis. Next, we classified top news channels shared via tweets. Lastly, we conducted a content analysis of news frames used in the top-shared sources.
Results: The network analysis suggests that the spread of information was faster in the Coronavirus network than in the other networks (Corona19, Shincheon, and Daegu). People who used the word “Coronavirus” communicated more frequently with each other. The spread of information was faster, and the diameter value was lower than for those who used other terms. Many of the news items highlighted the positive roles being played by individuals and groups, directing readers’ attention to the crisis. Ethical issues such as deviant behavior among the population and an entertainment frame highlighting celebrity donations also emerged often. There was a significant difference in the use of nonportal (n=14) and portal news (n=26) sites between the four network types. The news frames used in the top sources were similar across the networks (P=.89, 95% CI 0.004-0.006). Tweets containing medically framed news articles (mean 7.571, SD 1.988) were found to be more popular than tweets that included news articles adopting nonmedical frames (mean 5.060, SD 2.904; N=40, P=.03, 95% CI 0.169-4.852).
Conclusions: Most of the popular news on Twitter had nonmedical frames. Nevertheless, the spillover effect of the news articles that delivered medical information about COVID-19 was greater than that of news with nonmedical frames. Social media network analytics cannot replace the work of public health officials; however, monitoring public conversations and media news that propagates rapidly can assist public health professionals in their complex and fast-paced decision-making processes.

/pdf/conversations-and-medical-news-frames-on-twitter-1yxm4veddd.pdf

Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea.

Comparing Twitter and YouTube networks in information diffusion: The case of the “Occupy Wall Street” movement

This paper examines communication patterns of key Twitter users by considering the socially and politically controversial Sejong City issue in South Korea. The network and message data were drawn from twtkr.com. Social network-based indicators and visualization methods were used to analyze political discourse among key Twitter users over time and illustrate various types of Tweets by these users and the interconnection between these key users. In addition, the study examines general Twitter users’ participation in the discussion on the issue. The results indicate that some Twitter profiles of media outlets tend to be very dominant in terms of their message output, whereas their Tweets are not likely to be circulated by other users. Noteworthy is that Twitter profiles of individuals who are geographically affiliated with the issue are likely to play an important role in the flow of communication.

Political Discourse Among Key Twitter Users: The Case Of Sejong City In South Korea

Cyworld, a Korean social networking site (SNS), enables politicians to establish and maintain their online presence and allows them to communicate with constituents through their personal profile. This article identifies the most visible politicians on Cyworld in terms of comments posted on their profiles between April 1, 2008 and June 14, 2009 and examines the text and sentiments reflected in those profiles. A content analysis combining semantic network analysis and sentiment analysis illustrates the meaning and collective sentiment of the comments. The results suggest that progressivism dominated political discourse and that the members of the ruling party received more negative comments than those of the opposition party. Furthermore, group-oriented terms indicated the existence of collectivism, which is representative of Eastern culture. The results suggest a significant relationship among gender, comment types, and SNS activities.

Networked Politics on Cyworld: The Text and Sentiment of Korean Political Profiles

This article explores how mobile phones function as an affective technology for young adults, by adapting the self-expansion model to understand attachment to mobile phones In an online survey, 272 smart phone users reported on their recalled responses to loss/separation from their mobile phone (not by choice), as well as their use of the mobile phone for self-expansion Results show that self-expansion via mobile phone was associated with greater inclusion of the mobile phone in the self-concept and greater subjective well-being Most respondents reported negative feelings, such as loneliness/disconnection, anxiety, and boredom, when without their mobile phone, but others felt relieved to be out of touch with others The use of the mobile phone for self-expansion was associated with more negative emotion and less positive emotion (relief) in response to loss/separation from the phone Interpretations of the findings are discussed

Se Jung Park

Papers

Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea.

Comparing Twitter and YouTube networks in information diffusion: The case of the “Occupy Wall Street” movement

Political Discourse Among Key Twitter Users: The Case Of Sejong City In South Korea

Networked Politics on Cyworld: The Text and Sentiment of Korean Political Profiles

“I miss my mobile phone!”: Self-expansion via mobile phone and responses to phone loss