Original Tweets Research Articles

#BoomerDoomer and #BoomerRemover

A chronological-age ‘risk group’ has been singled out since the onset of the current pandemic due to the higher risk of serious illness and mortality from COVID-19 closely associated with ‘older age’. While this categorisation does not presuppose discrimination in itself, it appears to be a precondition for discursive discrimination. COVID-19 age-related risks have particularly exacerbated ageism on social media, fuelling discourses driven by intergenerational tension. Despite the importance of social media in contemporary communication, there is still scant research on the representation of ageism in these environments. This paper attempts to fill this void by exploring millennials’ use of the derogatory Twitter hashtags #BoomerDoomer and #BoomerRemover to discriminate ‘baby boomers’ as the COVID-19 risk group. Informed by Critical Discourse Analysis (CDA) showing interest in discriminatory discourse in the media, the study aims at disclosing the main themes and discursive strategies used to construe ageist content in a sample of original tweets posted during the initial stage of the disease outbreak when the hashtags were trending. Framed by a CDA taxonomy of discriminatory discursive strategies, the combined thematic and discourse analysis highlights how the chosen topics and discursive strategies subtly perpetuate ageism, thus suggesting its resurgence in the COVID-19 era.

Fine-Tuning BERT Models to Classify Misinformation on Garlic and COVID-19 on Twitter.

Garlic-related misinformation is prevalent whenever a virus outbreak occurs. With the outbreak of COVID-19, garlic-related misinformation is spreading through social media, including Twitter. Bidirectional Encoder Representations from Transformers (BERT) can be used to classify misinformation from a vast number of tweets. This study aimed to apply the BERT model for classifying misinformation on garlic and COVID-19 on Twitter, using 5929 original tweets mentioning garlic and COVID-19 (4151 for fine-tuning, 1778 for test). Tweets were manually labeled as ‘misinformation’ and ‘other.’ We fine-tuned five BERT models (BERTBASE, BERTLARGE, BERTweet-base, BERTweet-COVID-19, and BERTweet-large) using a general COVID-19 rumor dataset or a garlic-specific dataset. Accuracy and F1 score were calculated to evaluate the performance of the models. The BERT models fine-tuned with the COVID-19 rumor dataset showed poor performance, with maximum accuracy of 0.647. BERT models fine-tuned with the garlic-specific dataset showed better performance. BERTweet models achieved accuracy of 0.897–0.911, while BERTBASE and BERTLARGE achieved accuracy of 0.887–0.897. BERTweet-large showed the best performance with maximum accuracy of 0.911 and an F1 score of 0.894. Thus, BERT models showed good performance in classifying misinformation. The results of our study will help detect misinformation related to garlic and COVID-19 on Twitter.

Discussions and Misinformation About Electronic Nicotine Delivery Systems and COVID-19: Qualitative Analysis of Twitter Content.

BackgroundMisinformation and conspiracy theories related to COVID-19 and electronic nicotine delivery systems (ENDS) are increasing. Some of this may stem from early reports suggesting a lower risk of severe COVID-19 in nicotine users. Additionally, a common conspiracy is that the e-cigarette or vaping product use–associated lung injury (EVALI) outbreak of 2019 was actually an early presentation of COVID-19. This may have important public health ramifications for both COVID-19 control and ENDS use.ObjectiveTwitter is an ideal tool for analyzing real-time public discussions related to both ENDS and COVID-19. This study seeks to collect and classify Twitter messages (“tweets”) related to ENDS and COVID-19 to inform public health messaging.MethodsApproximately 2.1 million tweets matching ENDS-related keywords were collected from March 1, 2020, through June 30, 2020, and were then filtered for COVID-19–related keywords, resulting in 67,321 original tweets. A 5% (n=3366) subsample was obtained for human coding using a systematically developed codebook. Tweets were coded for relevance to the topic and four overarching categories.ResultsA total of 1930 (57.3%) tweets were coded as relevant to the research topic. Half (n=1008, 52.2%) of these discussed a perceived association between ENDS use and COVID-19 susceptibility or severity, with 42.4% (n=818) suggesting that ENDS use is associated with worse COVID-19 symptoms. One-quarter (n=479, 24.8%) of tweets discussed the perceived similarity/dissimilarity of COVID-19 and EVALI, and 13.8% (n=266) discussed ENDS use behavior. Misinformation and conspiracy theories were present throughout all coding categories.ConclusionsDiscussions about ENDS use and COVID-19 on Twitter frequently highlight concerns about the susceptibility and severity of COVID-19 for ENDS users; however, many contain misinformation and conspiracy theories. Public health messaging should capitalize on these concerns and amplify accurate Twitter messaging.

Quantifying Changes in the Language Used Around Mental Health on Twitter Over 10 Years: Observational Study.

BackgroundMental health challenges are thought to affect approximately 10% of the global population each year, with many of those affected going untreated because of the stigma and limited access to services. As social media lowers the barrier for joining difficult conversations and finding supportive groups, Twitter is an open source of language data describing the changing experience of a stigmatized group.ObjectiveBy measuring changes in the conversation around mental health on Twitter, we aim to quantify the hypothesized increase in discussions and awareness of the topic as well as the corresponding reduction in stigma around mental health.MethodsWe explored trends in words and phrases related to mental health through a collection of 1-, 2-, and 3-grams parsed from a data stream of approximately 10% of all English tweets from 2010 to 2021. We examined temporal dynamics of mental health language and measured levels of positivity of the messages. Finally, we used the ratio of original tweets to retweets to quantify the fraction of appearances of mental health language that was due to social amplification.ResultsWe found that the popularity of the phrase mental health increased by nearly two orders of magnitude between 2012 and 2018. We observed that mentions of mental health spiked annually and reliably because of mental health awareness campaigns as well as unpredictably in response to mass shootings, celebrities dying by suicide, and popular fictional television stories portraying suicide. We found that the level of positivity of messages containing mental health, while stable through the growth period, has declined recently. Finally, we observed that since 2015, mentions of mental health have become increasingly due to retweets, suggesting that the stigma associated with the discussion of mental health on Twitter has diminished with time.ConclusionsThese results provide useful texture regarding the growing conversation around mental health on Twitter and suggest that more awareness and acceptance has been brought to the topic compared with past years.

#NotHolidayButDistance Education: a study on social media use for K-12 education during the COVID-19 pandemic

PurposeThis study aims to explore Twitter posts of Turkish government agencies and the public under a specific hashtag, #NotHolidayButDistanceEducation, specifically related to online distance education during the Covid-19 pandemic.Design/methodology/approachThis study used a thematic analysis on 22,547 original tweets posted by 6,970 users during the first month of online distance education in Turkish K-12 schools. Based on like and retweet counts, the study further explored the extent of stakeholders’ engagement with the observed themes.FindingsThe findings showed that government agencies and citizens used Twitter to provide technical and psychological support, appreciate and motivate stakeholders, demonstrate sample distance education activities, share information and offer suggestions about the ongoing online distance education. It was also observed that the hashtag has been used for expressing negative views about online distance education and for political purposes. A positive relationship was found between social media engagement and providing technical support or sharing information for online distance education.Practical implicationsThis study highlights the role of social media in providing practical and emotional support to education stakeholders in times of crisis. Thus, governments can use social media to provide evidence-based psychological and physical health support to their citizens during a pandemic. Social media can serve to improve education practices in schools through the interactions between the public and policymakers.Originality/valueTo the best of the authors’ knowledge, this study can be considered unique because it demonstrates the civic use of social media for educational crisis management. This study highlights the influence of social media in educational policy and practice development in the contemporary era.

Puff Bars, Tobacco Policy Evasion, and Nicotine Dependence: Content Analysis of Tweets.

BackgroundPuff Bars are e-cigarettes that continued marketing flavored products by exploiting the US Food and Drug Administration exemption for disposable devices.ObjectiveThis study aimed to examine discussions related to Puff Bar on Twitter to identify tobacco regulation and policy themes as well as unanticipated outcomes of regulatory loopholes.MethodsOf 8519 original tweets related to Puff Bar collected from July 13, 2020, to August 13, 2020, a random 20% subsample (n=2661) was selected for qualitative coding of topics related to nicotine dependence and tobacco policy.ResultsOf the human-coded tweets, 2123 (80.2%) were coded as relevant to Puff Bar as the main topic. Of those tweets, 698 (32.9%) discussed tobacco policy, including flavors (n=320, 45.9%), regulations (n=124, 17.8%), purchases (n=117, 16.8%), and other products (n=110, 15.8%). Approximately 22% (n=480) of the tweets referenced dependence, including lack of access (n=273, 56.9%), appetite suppression (n=59, 12.3%), frequent use (n=47, 9.8%), and self-reported dependence (n=110, 22.9%).ConclusionsThis study adds to the growing evidence base that the US Food and Drug Administration ban of e-cigarette flavors did not reduce interest, but rather shifted the discussion to brands utilizing a loophole that allowed flavored products to continue to be sold in disposable devices. Until comprehensive tobacco policy legislation is developed, new products or loopholes will continue to supply nicotine demand.

Applying diffusion of innovations theory to social networks to understand the stages of adoption in connective action campaigns

This research proposes a conceptual framework for determining the adoption trajectory of information diffusion in connective action campaigns. This approach reveals whether an information campaign is accelerating, reached critical mass, or decelerating during its life cycle. The experimental approach taken in this study builds on the diffusion of innovations theory, critical mass theory, and previous s-shaped production function research to provide ideas for modeling future connective action campaigns. Most social science research on connective action has taken a qualitative approach. There are limited quantitative studies, but most focus on statistical validation of the qualitative approach, such as surveys, or only focus on one aspect of connective action. In this study, we extend the social science research on connective action theory by applying a mixed-method computational analysis to examine the affordances and features offered through online social networks (OSNs) and then present a new method to quantify the emergence of these action networks. Using the s-curves revealed through plotting the information campaigns usage, we apply a diffusion of innovations lens to the analysis to categorize users into different stages of adoption of information campaigns. We then categorize the users in each campaign by examining their affordance and interdependence relationships by assigning retweets, mentions, and original tweets to the type of relationship they exhibit. The contribution of this analysis provides a foundation for mathematical characterization of connective action signatures, and further, offers policymakers insights about campaigns as they evolve. To evaluate our framework, we present a comprehensive analysis of COVID-19 Twitter data. Establishing this theoretical framework will help researchers develop predictive models to more accurately model campaign dynamics.

The bias of Twitter as an agenda-setter on COVID-19: An empirical research using log data and survey data in Japan

This study investigates the bias of Twitter as an agenda-setter during COVID-19. Specifically, we analyze the agenda-setting function of Twitter (Study 1) and characteristics of information disseminators on Twitter, agenda-builders (Study 2), related to the COVID-19 pandemic. In Study 1, we examined rank correlations between the media agendas on COVID-19 and public agendas. The results indicated that Twitter agendas resonate with those who have liberal tendencies. In Study 2, we used data from the Internet survey to identify the political attitudes of agenda-builders who tweet or retweet on COVID-19. The results of the model analyses indicated that people with liberal tendencies, motivated by their political attitude, created original tweets, and some of those tweets were then retweeted by flaming-oriented people driven by a sense of justice. This seems to be how information about COVID-19 spreads on Twitter in Japan.

How does duplicate tweeting boost social media exposure to scholarly articles?

• Few studies disclose how duplicate tweeting strategy boosts social media exposure to online information, especially to scientific publications. • Several novel metrics are proposed to measure the behavior of duplicate tweeting. • Social media exposures to scholarly articles have significant marginal effects as the number of duplicate tweeting increases. • The interval of duplicate tweeting is negatively correlated with social media exposure to scholarly articles. • Posting non-duplicate content can bring more online exposure to articles, facilitating more discussion or conversations. Duplicate tweeting is a kind of behavior that a user posts the same or substantially similar original tweets to one or several topics multiple times in a row, which is commonly used to boost social media exposure to online information. However, how this behavior boosts social media exposure to online information, especially to scientific publications, is still unclear. In this study, we use the number of retweets an article received as a proxy for social media exposure to the article. Based on the duplicate tweeting records of 12,263 users on Twitter from 2011 to 2017, we find that social media exposure to scholarly articles has a significant marginal effect as the number of duplicate tweeting ( k ) increases. It ramps up a peak value when k is between 2 and 4 and goes down when k takes a greater value. We also find that a longer posting interval can sharply reduce social media exposure to scholarly articles, and posting non-duplicate content can significantly improve the exposure. Our findings not only help scientists and journal publishers effectively improve the social impact of their research outputs but also advance the understanding of the diffusion processes of scholarly articles.

Facing the volatility of tweets in altmetric research

AbstractThe data re‐collection for tweets from data snapshots is a common methodological step in Twitter‐based research. Understanding better the volatility of tweets over time is important for validating the reliability of metrics based on Twitter data. We tracked a set of 37,918 original scholarly tweets mentioning COVID‐19‐related research daily for 56 days and captured the reasons for the changes in their availability over time. Results show that the proportion of unavailable tweets increased from 1.6 to 2.6% in the time window observed. Of the 1,323 tweets that became unavailable at some point in the period observed, 30.5% became available again afterwards. “Revived” tweets resulted mainly from the unprotecting, reactivating, or unsuspending of users' accounts. Our findings highlight the importance of noting this dynamic nature of Twitter data in altmetric research and testify to the challenges that this poses for the retrieval, processing, and interpretation of Twitter data about scientific papers.

Drunk Girl: A brief thematic analysis of Twitter posts about alcohol use and #MeToo

This research brief presents analyses aimed at characterizing the content of a corpus of original and retweeted Twitter posts related to the role of alcohol intoxication in sexual assault within the context of the #MeToo movement. A text-mining approach was used to collect 25,014 tweets and retweets containing #MeToo and keywords involving alcohol intoxication posted during the year after #MeToo went viral (October 15, 2017 to October 15, 2018). A combination of algorithm-driven cluster analysis and manual qualitative analysis was used to discover the most prominent themes. Analyses revealed that the content of tweets related to alcohol use and #MeToo were overwhelmingly centered around U.S.politics. Analysis of a subsample of 5,566 original (i.e., non-retweeted) tweets revealed fewer politically-focused topics and a noteworthy theme expressing concern for men in the #MeToo era. Findings are discussed in relation to political discourse on social media, the use of #MeToo to express political ideologies, and implications for supporting sexual assault survivors.

Twitter Mediated Sociopolitical Communication During the COVID-19 Pandemic Crisis in India.

While Twitter has grown popular among political leaders as a means of computer-mediated mass media communication alternative, the COVID-19 pandemic required new strategies for socio-political communication to handle such a crisis. Using the case of India, which was one of the worst-hit countries and is also the world’s largest democracy, this research explicates how political leaders responded to the COVID-19 crisis on Twitter during the first wave as it was the first time such a crisis occurred. Theoretical frameworks of discursive leadership and situational crisis communication theory have been used to analyze interactions based on the usage patterns, the content of communication, the extent of usage in relation to the severity of the crisis, and the possible role of leaders’ position along with the status of their political party. The sample consisted of tweets posted by six prominent political leaders in India across the four consecutive lockdown periods from 25th March to 31st May 2020. A total of 4,158 tweets were scrapped and after filtering for retweets, the final dataset consisted of 2,809 original tweets. Exploratory data analysis, sentiment analysis, and content analysis were conducted. It was found that the tweets had an overall positive sentiment, an important crisis management strategy. Four main themes emerged: crisis management information, strengthening followers’ resilience and trust, reputation management, and leaders’ proactiveness. By focusing on such discursive aspects of crisis management, the study comprehensively highlights how political interactions on twitter integrated with politics and governance to handle COVID-19 in India. The study has implications for the fields of digital media interaction, political communication, public relations, and crisis leadership.

The Spread of COVID-19 Crisis Communication by German Public Authorities and Experts on Twitter: Quantitative Content Analysis.

BackgroundThe COVID-19 pandemic led to the necessity of immediate crisis communication by public health authorities. In Germany, as in many other countries, people choose social media, including Twitter, to obtain real-time information and understanding of the pandemic and its consequences. Next to authorities, experts such as virologists and science communicators were very prominent at the beginning of German Twitter COVID-19 crisis communication.ObjectiveThe aim of this study was to detect similarities and differences between public authorities and individual experts in COVID-19 crisis communication on Twitter during the first year of the pandemic.MethodsDescriptive analysis and quantitative content analysis were carried out on 8251 original tweets posted from January 1, 2020, to January 15, 2021. COVID-19–related tweets of 21 authorities and 18 experts were categorized into structural, content, and style components. Negative binomial regressions were performed to evaluate tweet spread measured by the retweet and like counts of COVID-19–related tweets.ResultsDescriptive statistics revealed that authorities and experts increasingly tweeted about COVID-19 over the period under study. Two experts and one authority were responsible for 70.26% (544,418/774,865) of all retweets, thus representing COVID-19 influencers. Altogether, COVID-19 tweets by experts reached a 7-fold higher rate of retweeting (t8,249=26.94, P<.001) and 13.9 times the like rate (t8,249=31.27, P<.001) compared with those of authorities. Tweets by authorities were much more designed than those by experts, with more structural and content components; for example, 91.99% (4997/5432) of tweets by authorities used hashtags in contrast to only 19.01% (536/2819) of experts’ COVID-19 tweets. Multivariate analysis revealed that such structural elements reduce the spread of the tweets, and the incidence rate of retweets for authorities’ tweets using hashtags was approximately 0.64 that of tweets without hashtags (Z=–6.92, P<.001). For experts, the effect of hashtags on retweets was insignificant (Z=1.56, P=.12).ConclusionsTwitter data are a powerful information source and suitable for crisis communication in Germany. COVID-19 tweet activity mirrors the development of COVID-19 cases in Germany. Twitter users retweet and like communications regarding COVID-19 by experts more than those delivered by authorities. Tweets have higher coverage for both authorities and experts when they are plain and for authorities when they directly address people. For authorities, it appears that it was difficult to win recognition during COVID-19. For all stakeholders studied, the association between number of followers and number of retweets was highly significantly positive (authorities Z=28.74, P<.001; experts Z=25.99, P<.001). Updated standards might be required for successful crisis communication by authorities.

Harass, mislead, & polarize: An analysis of Twitter political bots’ tactics in targeting the immigration debate before the 2018 U.S. midterm election

ABSTRACT This study investigates the interaction and messaging tactics of political Twitter bots before an election. We analyzed the strategies of influential bots seeking to affect the immigration debate before the 2018 U.S. midterm elections. Our findings reveal that the 10 most influential bots in our dataset all presented an anti-immigration viewpoint, and both posted original tweets and retweeted other bot accounts’ tweets to give a false sense of authenticity and anti-immigration consensus. Bots’ messages relied heavily on negative emotional appeals by spreading harassing language and disinformation likely intended to evoke fear toward immigrants. Such accounts also employed polarizing language to entrench political group identity and provoke partisanship. Our findings help to understand the interaction and messaging tactics employed by political bots and suggest potential strategies that may be employed to counter their effectiveness.

A high-resolution temporal and geospatial content analysis of Twitter posts related to the COVID-19 pandemic.

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.

Journalists and Engagement on Twitter and Climate Change: Tweet Authors, Formats, and Content During COP25

ABSTRACT The objective of this research is to analyze the engagement generated by tweets published from verified accounts during the COP25, and to see what place journalists occupy in the communicative flow during the event. The original tweets labeled “#cop25” and published by media, individual journalists, and other users such as NGOs and international organizations among others, were downloaded during the summit. Through a content analysis they were classified according to the type of author, the format, the type of content, and the transmitted sentiment. After calculating their engagement, significant differences were identified using non-parametric statistical tests. The results show that the tweets that generated the most engagement are those published by journalists, those that contain only text, and those that present a negative sentiment. One of the most relevant findings is that journalists can be key pieces in the communication and awareness of climate change if they are more active on Twitter. If, however, they maintain a much lower number of posts than other users, it becomes paradoxical in that those who achieve a more effective impact are not sufficiently exploiting the potential of Twitter.

Twitter, Telepractice, and the COVID-19 Pandemic: A Social Media Content Analysis.

Purpose Telepractice was extensively utilized during the COVID-19 pandemic. Little is known about issues experienced during the wide-scale rollout of a service delivery model that was novel to many. Social media research is a way to unobtrusively analyze public communication, including during a health crisis. We investigated the characteristics of tweets about telepractice through the lens of an established health technology implementation framework. Results can help guide efforts to support and sustain telehealth beyond the pandemic context. Method We retrieved a historical Twitter data set containing tweets about telepractice from the early months of the pandemic. Tweets were analyzed using a concurrent mixed-methods content analysis design informed by the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework. Results Approximately 2,200 Twitter posts were retrieved, and 820 original tweets were analyzed qualitatively. Volume of tweets about telepractice increased in the early months of the pandemic. The largest group of Twitter users tweeting about telepractice was a group of clinical professionals. Tweet content reflected many, but not all, domains of the NASSS framework. Conclusions Twitter posting about telepractice increased during the pandemic. Although many tweets represented topics expected in technology implementation, some represented phenomena were potentially unique to speech-language pathology. Certain technology implementation topics, notably sustainability, were not found in the data. Implications for future telepractice implementation and further research are discussed.

Suicidality Detection on Social Media Using Metadata and Text Feature Extraction and Machine Learning

In this study, we implemented machine learning models that can detect suicidality posts on Twitter. We randomly selected and annotated 20,000 tweets and explored metadata and text features to build effective models. Metadata features were studied in great details to understand their possibility and importance in suicidality detection models. Results showed that posting type (i.e., reply or not) and time-related features such as the month, day of the week, and the time (AM vs. PM) were the most important metadata features in suicidality detection models. Specifically, the probability of a social media post being suicidal is higher if the post is a reply to other users rather than an original tweet. Moreover, tweets created in the afternoon, on Fridays and weekends, and in fall have higher probabilities of being detected as suicidality tweets compared with those created in other times. By integrating metadata and text features, we obtained a model of good performance (i.e., F1 score of 0.846) that can assist humans in the real-world setting to detect suicidality social media posts.

Public library Twitter use during the early period of the COVID-19 lockdown in the United States

PurposeThe purpose of this study is to explore the Twitter use of public libraries during the early period of lockdown due to the COVID-19 pandemic to evaluate the focus of Twitter communication.Design/methodology/approachA total of 57 active, public library Twitter accounts were used for data collection and analysis. The tweets examined were a combination of original tweets (n = 1,465) and retweets (n = 516) posted from other Twitter accounts on the public libraries’ Twitter feeds. A content analysis scheme was developed to analyse topical aspects of the tweets.FindingsThe most frequent tweets were about library events, programmes and activities. However, there was a relatively low focus on sharing community information and addressing information related to the pandemic. The study suggests that public libraries could use Twitter to provide library resources and services to their patrons, whilst also acting as a virtual community centre safely keeping patrons engaged in the face of a global pandemic. By doing so, Twitter could be used as an integral part of promoting the mission of public libraries.Research limitations/implicationsThe study examined a limited number of public libraries’ Twitter posts. Whilst the study carried out a random sampling of 10% of public libraries from the five states that had the highest COVID-19 cases in the month of April 2020, the study only examined tweets of 57 public libraries being active in posting. Thus, the findings of the study are not for generalizing.Practical implicationsThe content scheme includes content types regarding library services and community information. The content category scheme is general to reflect themes of content during a normal time and any emergency. Thus, this framework could be helpful for the content development of public libraries in planning social media use.Originality/valueThe study used a new content analysis framework to examine both original tweets and retweets for information sharing of library services and community information. The approach of content analysis is distinctive to examine libraries’ communication trends on social media not only in normal times but also in times of crisis as well. The study also incorporated additional measures to assess Twitter practices including hashtags.

Analysing Information Diffusion in Natural Hazards using Retweets - a Case Study of 2018 Winter Storm Diego

ABSTRACT Information diffusion on social media during disasters is an important indicator of community resilience. As a common natural hazard in the U.S., winter storms often cause adverse socio-economic impacts on human society. Understanding people’s perception and behaviours during winter storms is important to mitigate negative impacts and promote community resilience. This study applies text mining and spatial analysis methods on Twitter data during Winter Storm Diego on 2018 December. Different from previous studies focusing on original tweets, this study utilized retweets to model information diffusion in the contiguous United States and analysed the geographic distribution of information flows in various topics. The diffusion extent and direction of the storm-related retweets were compared among different topics. Kernel density maps and standard deviational ellipse were applied to model the spatial distribution of the retweets in different topics. The result shows that people outside of the affected areas expressed more negative sentiment towards the storm than people in the affected areas. Also, distance decay of retweet density has been found and the decay rate differs in different topics. These findings of the analyses will provide support for disaster relief, information communication and broadcasting through social media platforms.