Crisis Communications on Social Media: Insights from Canadian Officials Twitter Presence during COVID-19 Pandemic

COVID-19 pandemic is a unique case in crisis management given its length, scale, several different response systems, and public officials' extensive social media use for crisis communication. Leveraging text mining techniques, we examine Canadian officials' presence on Twitter during the pandemic by focusing on their COVID-19-related content. We identified eight themes of discussion that unveil 37 relevant subthemes. Concentrating on the COVID-19-addressing themes, we reveal that educating citizens on the safety information and keeping them informed with the latest crisis information was the Canadian officials' primary focus during the pandemic. To fight COVID-19, Canadian officials used four policies, and to implement those, they promoted eight measures and practices. According to the volume of generated content, the evolution of COVID-19-addressing themes over time, and their coexistence;Test and trace was the most advocated policy by emphasizing screening the symptoms. To stop the spread of COVID-19, Canadian officials promoted wearing Mask, Social distancing, Hand washing, and Stay home, where Mask and Social distancing were the most frequent practices. Our study contributes to crisis communication and management by depicting how Canadian officials leveraged social media during such a big-scale crisis. © 2022 IEEE Computer Society. All rights reserved.

What Really Drives the Spread of COVID-19 Tweets: A Revisit from Perspective of Content

COVID-19 content spreads wildly on social media and produces significant effects in both causing social panic and assisting pandemic management. However, what really enhances the diffusion of pandemic-related content during COVID-19, particularly from the perspective of the content itself, remains unexplored. Using large-scale COVID-19 tweets posted on Twitter, this paper empirically examines the effects of the four key characteristics, namely emotions, topics, hashtags, and mentions, on information spread in the pandemic. The empirical results show that most negative emotions have positive effects on retweeting. Nevertheless, the positive effect of trust on retweeting is unexpectedly the strongest. And the positive effects of the political topics and mentioning politicians further indicate that people are sensitive to the politicization of information during the pandemic. The strongest anger intensity in the political topic also needs to be noticed. The results complement the extant understanding of information diffusion during COVID-19 and provide insights for the governments to understand the psychology and behavior of large population during disasters like global pandemics. © 2022 IEEE.

Using Deep Learning to Identify Linguistic Features that Facilitate or Inhibit the Propagation of Anti- and Pro-Vaccine Content on Social Media

Anti-vaccine content is rapidly propagated via social media, fostering vaccine hesitancy, while pro-vaccine content has not replicated the opponent's successes. Despite this dis-parity in the dissemination of anti- and pro-vaccine posts, linguistic features that facilitate or inhibit the propagation of vaccine-related content remain less known. Moreover, most prior machine-learning algorithms classified social-media posts into binary categories (e.g., misinformation or not) and have rarely tackled a higher-order classification task based on divergent perspectives about vaccines (e.g., anti-vaccine, pro-vaccine, and neutral). Our objectives are (1) to identify sets of linguistic features that facilitate and inhibit the propagation of vaccine-related content and (2) to compare whether anti-vaccine, pro-vaccine, and neutral tweets contain either set more frequently than the others. To achieve these goals, we collected a large set of social media posts (over 120 million tweets) between Nov. 15 and Dec. 15, 2021, coinciding with the Omicron variant surge. A two-stage framework was developed using a fine-tuned BERT classifier, demonstrating over 99 and 80 percent accuracy for binary and ternary classification. Finally, the Linguistic Inquiry Word Count text analysis tool was used to count linguistic features in each classified tweet. Our regression results show that anti-vaccine tweets are propagated (i.e., retweeted), while pro-vaccine tweets garner passive endorsements (i.e., favorited). Our results also yielded the two sets of linguistic features as facilitators and inhibitors of the propagation of vaccine-related tweets. Finally, our regression results show that anti-vaccine tweets tend to use the facilitators, while pro-vaccine counterparts employ the inhibitors. These findings and algorithms from this study will aid public health officials' efforts to counteract vaccine misinformation, thereby facilitating the delivery of preventive measures during pandemics and epidemics. © 2022 IEEE.

THE COVID-19 PANDEMIC IMPACT ON THE TV COMMERCIALS CONTENT IN EUROPEAN COUNTRIES

The outbreak of the COVID-19 global pandemic has strongly affected different issues of everyday life. The pandemic has changed consumer attitudes and behavior. Consequently, during COVID-19, businesses had to decide how to organize the advertising campaigns and what content should be addressed to consumers. Considering the limited number of articles concerning these issues, it seems important to evaluate the impact of the global health crisis on advertising messages in different countries. The main objective of this paper is to assess TV commercial campaigns implemented during the COVID-19 pandemic, including the specific epidemic situation in the selected countries. The research methodology was based on the observation, a qualitative method of collecting data obtained through monitoring the content of TV commercials aired during the afternoon, before or after the main news service. The research was conducted in May 2020 and in May 2021 in five selected European countries affected to varying degrees by the coronavirus pandemic. The research results allowed evaluation of the advertising messages in terms of their content and values exposed. The findings indicate that the number of COVID-related TV commercials is not correlated with the number of patients and deaths from COVID-19. The themes appearing the most often in the TV commercials in the studied period were the #StayAtHome idea, family and friends, the aspect of safety during the crisis, and brands' special offers. The tone of the advertisements was usualy uplifting and hopeful, trying to cheer the stressed societies. From a theoretical perspective, the research results show the advertising strategy issues in specific conditions in different European countries. These findings concerning TV commercial strategy during a pandemic crisis could provide references for other countries, especially in Europe. The research results could be useful for marketing managers in developing strategies concerning the successful planning of TV commercials in crisis periods in European countries.

COVID-19 Vaccine Sensing: Sentiment Analysis from Twitter Data

The COVID-19 outbreak a pandemic, which poses a serious threat to global public health and lead to a tsunami of online social media. Individuals frequently express their views, opinions and emotions about the events of the pandemic on Twitter, Facebook, etc. Many researches try to analyze the sentiment of the COVID-19-related content from these social networks. However, they have rarely focused on the vaccine. In this paper, we study the COVID-19 vaccine topic from Twitter. Specifically, all the tweets related to COVID-19 vaccine from December 15th, 2020 to February 10th, 2021 are collected by using the Twitter API, then the unsupervised learning VADER model is used to judge the emotion categories (positive, neutral, negative) and calculate the sentiment value of the dataset. Based on the interaction between users, a communication topological network is constructed and the emotional direction is explored. We find that people had different sentiments between Chinese vaccine and those in other countries. The sentiment value might be affected by the number of daily news cases and deaths, the nature of key issues in the communication network. And revealing that the key nodes in the social network can produce emotional contagion to other nodes. © 2021 IEEE.

Characterization of Domestic Violence through Self-disclosure in Social Media: A Case Study of the Time of COVID-19

Domestic violence (DV) can lead to physical, psychological, and/or emotional consequences for its victims. Social media provides a new platform for DV victims to share their personal experiences and seek needed support. The anonymity of social media can potentially provide comfort and safety for victims to disclose their victimization experience. Despite a few efforts in detecting DV from social media, they have focused on differentiating DV-from non-DV-related content, or classifying DV-related content into a few general categories. By conducting an in-depth analysis of the content of DV self-disclosure in social media, this study characterizes DV in multiple aspects for the first time, including victim, perpetrator, relationship, and abuse. Moreover, it identifies the attributes to describe each aspect in detail. Furthermore, we use the social media data generated during the COVID-19 pandemic as a case study to understand the patterns of DV. The research findings of this study have implications for increasing the awareness of DV and designing support for DV victims. © 2021 IEEE.

Unsupervised Keyword Combination Query Generation from Online Health Related Content for Evidence-Based Fact Checking

False information in the domain of online health related articles is of great concern, which can be witnessed in the current pandemic situation of Covid-19. It is markedly different from fake news in the political context as health information should be evaluated against the most recent and reliable medical resources such as scholarly repositories. However, one of the challenges with such an approach is the retrieval of the pertinent resources. In this work, we formulate a new unsupervised task of generating queries using keywords extracted from a health-related article which can be further applied to retrieve relevant authoritative and reliable medical content from scholarly repositories to assess the article's veracity. We propose a three-step approach for it and illustrate that our method is able to generate effective queries. We also curate a new dataset to aid the evaluation for this task which will be made available upon request. © 2021 ACM.

Posting Alcohol-Related Content and Texting Under the Influence Among Hispanic College Students.

Social media use and texting among college students often coincide with drinking. The present study investigated the associations between monthly alcohol use, social media habits, sharing alcohol references, and drunk texting among Hispanic college students. Participants (n = 620, 71.6% female; Mage = 21.07 years, SD = 3.60) completed an online survey containing: demographics, drug use frequency, Sharing of Alcohol-Related Content on Social Media Sites Scale (SARC), Texting Under the Influence Scale, Strategic Self Presentation Scale, Bergen Social Media Addiction Scale, and iPhone Screen Time. Bivariate correlations assessed relationships between all variables. Six logistic regressions assessed subscales of the SARC, and a linear regression assessed the Texting Under the Influence Scale. Almost 15% of participants met criteria for social media addiction, almost 40% reported ever sharing alcohol posts, and approximately 20% reported drunk texting at least once per month. Participants with iPhones averaged 16.84 weekly hours on social media (based on "screen time"). Frequently posting references of drinking alone was associated with more time on social media, higher social media addiction, and greater public sharing of alcohol content. Conversely, posting references of drinking at social gatherings was associated with privately sharing alcohol references and increased social media addiction. Drunk texting was related to increased age, greater Instagram use, decreased Facebook use, and privately sharing alcohol posts. Findings suggest patterns of drinking and sharing alcohol-related content to inform health promotion efforts, especially while many during COVID-19 are heightening use of alcohol and social media.