Volume Of Tweets Research Articles

Does pre-processing affect the correlation indicator between Twitter message volume and stock market trading volume?

Motivation: More and more authors empirically verify the relationship between the volume of tweets and the stock market indicators. The patterns explored from Twitter most often take the form of time series that represent user’s activity on different level of granularity (moods, emotions, relevant topic or query-related messages). Sentiment analysis is a technique used to transform text data into information on the mood and related behavioral categories. Supervised machine learning is the most commonly used approach to sentiment analysis. Thus, the results of an empirical analysis of the relationship between social media and stock depend on the quality of results of classification task. The quality of the features used to learn the classifier plays a key role. The feature space is modified using various data pre-processing scenarios that aim to increase accuracy of classification. The impact of pre-processing data on the quality of classification is often discussed in studies. Very few authors discuss the impact of pre-processing on the correlation indicator between Twitter and stock market. Aim: Analysis of the impact of tweets pre-processing on the Pearson correlation indicator between the mood of Twitter users and stock market trading volume. Results: The correlation between the volume of stock market trading and the volume of tweets has been empirically confirmed. The effect of pre-processing on the correlation index was noted for the variables ‘all_tweets’ and ‘negative_tweets’. This is because the training set has a significant amount of tweets with negation. However, the results are not conclusive. The differences between the Pearson correlation index calculated for scenario one and scenario four are not significant. However, this indicates that the effect of noise data may reduce the quality and precision of conclusions. Especially in the case of frequent repetition of a certain category of noise.

Performance Evaluation of Analytics Models for Trends Analysis of News

Microblogging services, especially Twitter, allow the user to share their most recent thoughts, feelings or news freely and almost immediately. Hence, the number of news tweets generated by the news media is increasing exponentially. Mining the valuable data from the large volume of tweets can help increase the revenue of organisations by allowing them to engage with the public faster and better by responding to the latest topics of interest. In this work, mining the hot keywords and being able in classifying the news tweets, trending topic and keywords in the news tweets. Both supervised and unsupervised machine learning models are used. Several machine learning algorithms are being used to compare the accuracy in classifying the tweets.

Sub-national longitudinal and geospatial analysis of COVID-19 tweets.

ObjectivesAccording to current reporting, the number of active coronavirus disease 2019 (COVID-19) infections is not evenly distributed, both spatially and temporally. Reported COVID-19 infections may not have properly conveyed the full extent of attention to the pandemic. Furthermore, infection metrics are unlikely to illustrate the full scope of negative consequences of the pandemic and its associated risk to communities.MethodsIn an effort to better understand the impacts of COVID-19, we concurrently assessed the geospatial and longitudinal distributions of Twitter messages about COVID-19 which were posted between March 3rd and April 13th and compared these results with the number of confirmed cases reported for sub-national levels of the United States. Geospatial hot spot analysis was also conducted to detect geographic areas that might be at elevated risk of spread based on both volume of tweets and number of reported cases.ResultsStatistically significant aberrations of high numbers of tweets were detected in approximately one-third of US states, most of which had relatively high proportions of rural inhabitants. Geospatial trends toward becoming hotspots for tweets related to COVID-19 were observed for specific rural states in the United States.DiscussionPopulation-adjusted results indicate that rural areas in the U.S. may not have engaged with the COVID-19 topic until later stages of an outbreak. Future studies should explore how this dynamic can inform future outbreak communication and health promotion.

Calibrating Human Attention as Indicator Monitoring #drought in the Twittersphere

AbstractState climatologists and other expert drought observers have speculated about the value of monitoring Twitter for #drought and related hashtags. This study statistically examines the relationships between the rate of tweeting using #drought and related hashtags, within states, accounting for drought status and news coverage of drought. We collected and geolocated tweets, 2017–18, and used regression analysis and a diversity statistic to explain expected and identify unexpected volumes of tweets. This provides a quantifiable means to detect state-weeks with a volume of tweets that exceeds the upper limit of the prediction interval. To filter out instances where a high volume of tweets is related to the activities of one person or very few people, a diversity statistic was used to eliminate anomalous state-weeks where the diversity statistic did not exceed the 75th percentile of the range for that state’s diversity statistic. Anomalous state-weeks in a few cases preceded the onset of drought but more often coincided with or lagged increases in drought. Tweets are both a means of sharing original experience and a means of discussing news and other recent events, and anomalous weeks occurred throughout the course of a drought, not just at the beginning. A sum-to-zero contrast coefficient for each state revealed a difference in the propensity of different states to tweet about drought, apparently reflecting recent and long-term experience in those states, and suggesting locales that would be most predisposed to drought policy innovation.

Growth of the Social #RadOnc Network on Twitter

PurposeTwitter is an increasingly popular social media platform within the health care community. The objective of this analysis is to characterize the profile of radiation oncology–related tweets and Twitter users over the past 6 years. Methods and MaterialsUsing the web-based social media analytics platform Symplur Signals, we filtered tweets containing at least 1 of the following hashtags or key words: #radonc, #radiationoncology, "rad onc," or "radiation oncology." We evaluated radiation oncology–related Twitter activity between October 2014 and March 2020 for tweet frequency, tweet content, and individuals or groups posting tweets. We identified the most influential Twitter users contributing to radiation oncology–related tweets. ResultsFrom 2014 to 2020, the quarterly volume of radiation oncology–related tweets increased from 5027 to 29,763. Physicians contributed the largest growth in tweet volume. Academic radiation oncologists comprise 60% of the most influential Twitter accounts responsible for radiation oncology–related content. The number of radiation-oncology resident physicians on Twitter increased from 25 to 328 over the past 6 years, and 20% of radiation-oncology residency programs have a Twitter account. Seventy-one percent of radiation oncology–related tweets generated direct communication via mentions, and 59% of tweets contain links to external sources, including scientific articles. ConclusionsThe number of physicians contributing radiation oncology–related Twitter content has increased significantly in recent years. Academic radiation oncologists are the primary influencers of radiation oncology–related Twitter activity. Twitter is used by radiation oncologists to both professionally network and discuss findings related to the field. There remains the opportunity for radiation oncologists to broaden their audience on Twitter to encompass a more diverse community, including patients.

From twitter to GDP: Estimating economic activity from social media

Using all geo-located image tweets shared on Twitter in 2012–2013, I find that the volume of tweets is a valid proxy for estimating GDP at the country level, explaining 78 percent of cross-country variations. I also exploit the geographic granularity of social media posts to estimate and predict GDP at the sub-national level. I find that tweets alone can explain 52 percent of the variation in GDP across cities in the US. Estimates using Twitter data perform on par with the more common night-lights proxy. Furthermore, both indicators seem to capture different aspects of economic activity and thus complement each other.

Geospatial correlation between COVID-19 health misinformation and poisoning with household cleaners in the Greater Boston Area

Introduction Calls to poison control about exposure to household cleaners have increased during the COVID-19 pandemic. This dynamic may reflect increased exposure from public health efforts as well as health misinformation shared on social media. Methods We analyzed the dynamics of calls to the Regional Center for Poison Control and Prevention serving Massachusetts and Rhode Island (MARI PCC) and tweets discussing treating COVID-19 with house cleaners from January 20, 2020 to May 5, 2020. We obtained publicly available tweets discussing the use of household cleaners to “cure COVID” from the same time period with geographic co-ordinates indicating that they were emitted from the Greater Boston Area. Results Our main finding is that public health efforts were followed by a sustained increase in calls after March 15, 2020 (10 ± 2 calls per day before to 15 ± 2.5 after) while misinformation on social media was associated with intermittent spikes in calls. Overall, calls significantly increased during the study period by 34% as compared to the previous 8 years, mostly reporting unintentional ingestions with no serious effects. The daily volume of tweets and retweets was significantly correlated with daily call rates to MARI PCC for the surrounding 7–10 days. Conclusions Health misinformation on social media about using household cleaning agents to treat COVID-19 and public health efforts lead to different dynamics in PCC calls. Public health efforts were followed by a sustained increase in calls after March 15, 2020 while misinformation on social media was followed by intermittent spikes in calls. This analysis is the first to link the geospatial dynamics of social media and public health interventions to poison center calls about exposure to household cleaners.

Twitter monitoring evidence of Covid-19 infodemic in Italy

BackgroundRisk communication is essential for risk management, especially during alarming events, in order to create a balanced risk perception. The tweets follow up can be useful to timely evidence “media storms” or “infodemics” thus suggesting corrective interventions. The spread of Covid-19 has been the occasion to observe the evolution of tweets about this topic, with the aim of evidence peaks of fearMethodsOn 30th January 2020 we started a large collection of Italian and English tweets containing the following keywords: “coronavirus”, “2019-nCov”, “CoronavirusOutbreak”, “CoronavirusWho”, “WuhanCoronavirus”, “coronaviruschina”, “coronavirusitalia”, “virus cina”, “virus cinese”, “virus mascherine”, “virus paura”, “virus polmonite” and “virus vaccine”. “Covid-19” and “SARS-CoV-2” were added later. For the data collection Twitter Streaming API was used.ResultsThe total number of tweets collected till 26th February was approximately 20M, 2M in Italian and 18M in English. The trend of the volume of tweets gathered shows a first small peak between Jan 30th and Feb 1st, but a second, higher, peak is visible after the 21st February, following the report of the Covid-19 clusters in Italy. Italian tweets account for 6% of the first peak and for 22% of the second. The Italian word “paura” firstly appeared on Feb 5th, with a peak after Feb 21th.Conclusions”infodemics” or “media storms”, are situations of uncontrolled diffusion of news and opinion, frequently leading to irrational and counterproductive behaviors. The Italian evolution of tweets indicate since the first day of cluster report, a clear escalation of fear, that suggests timely corrective actions to facilitate the disease control.Key messages With the twitter monitoring we demonstrate that since 21th February an infodemic is spreading in Italy.The twitter follow up can be used to timely evidence a public alarm, motivate corrective interventions and. evaluate the efficacy of them.

Mining public sentiments and perspectives from geotagged social media data for appraising the post-earthquake recovery of tourism destinations

Post-disaster recovery involves interdependent processes of physical and psychological rehabilitations. Over the past few years, researchers have explored geotagged social media data to assist the planning, monitoring, and assessment of the post-disaster recovery of tourism destinations, given its advantages over traditional approaches. Nonetheless, recent studies have mostly focused on quantitatively accessing the physical elements of post-disaster recovery (e.g., infrastructure reconstruction and re-influx of tourists). Few studies have explored people's sentiments and perspectives over the process of post-disaster recovery. In this study, a mixed methods approach involving sentiment analysis and Latent Dirichlet allocation (LDA) topic modeling is designed for mining sheer volume of tweets about Lombok and Bali, generated by nonlocal Twitter users after a series of earthquakes in the two places in August 2018. The findings mainly suggest that people have generally become less negative about Lombok and Bali over time, despite fluctuations in their sentiment polarities' central tendencies. In addition, dissatisfactions about the housing reconstruction progress, tourism recovery status, and living conditions in the affected areas of Lombok still existed in 2019; contestations have been found with regard to the huge funds for hosting the 2018 Bali IMF-World Bank meeting after the earthquakes. The overall results of this study have proved that the adopted approach can effectively reveal the variations of people's sentiments and perspectives of general and specific issues regarding post-disaster tourism recovery over time.

Temporal and Location Variations, and Link Categories for the Dissemination of COVID-19-Related Information on Twitter During the SARS-CoV-2 Outbreak in Europe: Infoveillance Study.

BackgroundThe spread of the 2019 novel coronavirus disease, COVID-19, across Asia and Europe sparked a significant increase in public interest and media coverage, including on social media platforms such as Twitter. In this context, the origin of information plays a central role in the dissemination of evidence-based information about the SARS-CoV-2 virus and COVID-19. On February 2, 2020, the World Health Organization (WHO) constituted a “massive infodemic” and argued that this situation “makes it hard for people to find trustworthy sources and reliable guidance when they need it.”ObjectiveThis infoveillance study, conducted during the early phase of the COVID-19 pandemic, focuses on the social media platform Twitter. It allows monitoring of the dynamic pandemic situation on a global scale for different aspects and topics, languages, as well as regions and even whole countries. Of particular interest are temporal and geographical variations of COVID-19–related tweets, the situation in Europe, and the categories and origin of shared external resources.MethodsTwitter’s Streaming application programming interface was used to filter tweets based on 16 prevalent hashtags related to the COVID-19 outbreak. Each tweet’s text and corresponding metadata as well as the user’s profile information were extracted and stored into a database. Metadata included links to external resources. A link categorization scheme—introduced in a study by Chew and Eysenbach in 2009—was applied onto the top 250 shared resources to analyze the relative proportion for each category. Moreover, temporal variations of global tweet volumes were analyzed and a specific analysis was conducted for the European region.ResultsBetween February 9 and April 11, 2020, a total of 21,755,802 distinct tweets were collected, posted by 4,809,842 distinct Twitter accounts. The volume of #covid19-related tweets increased after the WHO announced the name of the new disease on February 11, 2020, and stabilized at the end of March at a high level. For the regional analysis, a higher tweet volume was observed in the vicinity of major European capitals or in densely populated areas. The most frequently shared resources originated from various social media platforms (ranks 1-7). The most prevalent category in the top 50 was “Mainstream or Local News.” For the category “Government or Public Health,” only two information sources were found in the top 50: US Centers for Disease Control and Prevention at rank 25 and the WHO at rank 27. The first occurrence of a prevalent scientific source was Nature (rank 116).ConclusionsThe naming of the disease by the WHO was a major signal to address the public audience with public health response via social media platforms such as Twitter. Future studies should focus on the origin and trustworthiness of shared resources, as monitoring the spread of fake news during a pandemic situation is of particular importance. In addition, it would be beneficial to analyze and uncover bot networks spreading COVID-19–related misinformation.

Can social media distort price discovery? Evidence from merger rumors

We study whether social media can play a negative information role by impeding price discovery in the presence of highly speculative rumors. We focus on merger rumors, where most do not materialize. We find that merger rumors accompanied by greater Twitter activity elicit greater immediate market reaction even though rumor-related Twitter activity is unrelated to the probability of merger realization. The price distortion associated with tweet volume persists weeks after a rumor and reverses only after eight weeks. The price distortion is more pronounced for rumors tweeted by Twitter users with greater social influence, for target firms with low institutional ownership, and for rumors that supply more details. Our evidence suggests that social media can be a rumor mill that hinders the market's price discovery of potentially false information.

Spatial biases in crowdsourced data: Social media content attention concentrates on populous areas in disasters

The objective of this study is to examine and quantify the relationships among sociodemographic factors, damage claims, and social media attention on areas during natural disasters. Social media has become an important communication channel for people to share and seek situational information to learn of risks, to cope with community disruptions, and to support disaster response. Recent studies in disaster informatics have recognized the presence of bias in the representation of social media activity in areas affected by disasters. To explore related factors for such bias, existing studies have used geo-tagged tweets to assess the extent of social media activity in disaster-affected areas to evaluate whether vulnerable populations remain silent on social media. However, less than 1% of all tweets are actually geo-tagged; therefore, attempts to understand the representativeness of geotagged tweets to the general population have shown that certain populations are over- or underrepresented. To address this limitation, this study examined the attention given to locations based on social media content. The study conducted a content-based analysis to filter tweets related to 84 super-neighborhoods in Houston during Hurricane Harvey and 57 cities in North Carolina during Hurricane Florence. By examining the relationships among sociodemographic factors, the number of damage claims, and the volume of tweets, the results showed that social media attention concentrates in populous areas, independent of education, language, unemployment, and median income. The relationship between population and social media attention is characterized by a sub-linear power law, indicating a large variation among the sparsely populated areas. Using a machine-learning model to label the topics of the tweets, the results showed that social media users pay more attention to rescue- and donation-related information; nevertheless, the topic variation is consistent across areas with different levels of attention. These findings contribute to a better understanding of the spatial concentration of social media attention regarding posting and spreading situational information in disasters. The findings could inform emergency managers and public officials to effectively use social media data for equitable resource allocation and action prioritization.

Social Media Data Analytics on Telehealth During the COVID-19 Pandemic

Introduction: Physical distancing during the coronavirus Covid-19 pandemic has brought telehealth to the forefront to keep up with patient care amidst an international crisis that is exhausting healthcare resources. Understanding and managing health-related concerns resulting from physical distancing measures are of utmost importance.Objectives: To describe and analyze the volume, content, and geospatial distribution of tweets associated with telehealth during the Covid-19 pandemic.Methods: We inquired Twitter public data to access tweets related to telehealth from March 30, 2020 to April 6, 2020. We analyzed tweets using natural language processing (NLP) and unsupervised learning methods. Clustering analysis was performed to classify tweets. Geographic tweet distribution was correlated with Covid-19 confirmed cases in the United States. All analyses were carried on the Google Cloud computing service “Google Colab” using Python libraries (Python Software Foundation).Results: A total of 41,329 tweets containing the term “telehealth” were retrieved. The most common terms appearing alongside ‘telehealth’ were “covid”, “health”, “care”, “services”, “patients”, and “pandemic”. Mental health was the most common health-related topic that appeared in our search reflecting a high need for mental healthcare during the pandemic. Similarly, Medicare was the most common appearing health plan mirroring the accelerated access to telehealth and change in coverage policies. The geographic distribution of tweets related to telehealth and having a specific location within the United States (n=19,367) was significantly associated with the number of confirmed Covid-19 cases reported in each state (p<0.001).Conclusion: Social media activity is an accurate reflection of disease burden during the Covid-19 pandemic. Widespread adoption of telehealth-favoring policies is necessary and mostly needed to address mental health problems that may arise in areas of high infection and death rates.

Social media and disasters: human security, environmental racism, and crisis communication in Hurricane Irma response

ABSTRACT Social media have been widely recognized as critical communication channel in disaster situations. However, there is limited empirical investigation on how the intersecting issues of social order, environmental impacts, and crisis communication unfold from the perspective of a social media user. This study examines 60,449 tweets to and from the news media in Florida during and immediately after Hurricane Irma in September, 2017. Based on a critical review of the literature coupled with an eight-category coding scheme (including second-hand reporting, reporting on self-experience, requesting help, coordinating relief efforts, and expressing well wishes), the article assesses the content and timing of tweets before, during, and after the storm. It finds that thematically, twitter coverage not only covers the storm itself but pressing social issues such as looting, price gouging, the privileging of elites in rebuilding efforts, environmental vulnerability, and abandoning pets. Temporally, the volume of different tweets peaked and dropped at different stages; for example, tweets about personal experience peaked when the hurricane hit the ground while requests for help peaked in the days after the hurricane. The study allows for a better understanding of the sociological, environmental, and even social justice impacts and related disaster response through the use of social media.

Boosting Health Campaign Reach and Engagement Through Use of Social Media Influencers and Memes

Public health organizations are increasingly turning to social media as a channel for health campaign dissemination, as these platforms can provide access to “hidden” or at-risk audiences such as populations of color and youth. However, few studies systematically assess the effects of such campaigns in a competitive communication environment characterized by an influx of sophisticated tobacco product marketing. The objective of the current study is to investigate how content and source features of Twitter messages about truth® campaigns influence their popularity, support, and reach. Keyword rules were used to collect tweets related to each of the six campaigns from the Twitter Firehose posted between August 2014 and June 2016. Data were analyzed using a combination of supervised and unsupervised machine learning, keyword algorithms, and human coding. Tweets were categorized by source type (direct or truth®-owned social influencer; non-influencer). Tweet content was coded and classified for valence and campaign references (branded vs. non-branded or organic content). Message reach was calculated by source type and message type. Keyword filters captured 308,216 tweets posted by 225,912 Twitter users. Findings revealed that campaigns that utilized social influencers as message sources generated more campaign-branded and sharable content (e.g., campaign hashtags) and greater volume of tweets per day and reach per day. Influential users posted fewer organic messages and more branded/sharable content, generating greater reach compared to non-influencers. Oppositional messages decreased over time. Harnessing cultural elements endemic to social media, such as popular content creators (influencers) and messages (memes), is a promising strategy for improving health campaign interest and engagement.

Does social media elevate supply chain importance? An empirical examination of supply chain glitches, Twitter reactions, and stock market returns

AbstractWe build upon supply chain management and social media research by exploring the Twitter response to supply chain glitches and how this response may moderate the relationship between supply chain glitches and stock market returns. We analyze data on 213 supply chain glitches for 150 firms across 5 years and over 2 billion tweets on publicly traded firms. Leveraging event study methodology, we find support for our hypotheses. Using both volume and sentiment of tweets, we observe significant reactions on Twitter after supply chain glitches; tweet volume increases and tweets become more negative. We also find that Twitter reactions after a glitch accentuate the relationship between supply chain glitches and stock market returns, demonstrating how social media may elevate the prominence of supply chain problems. To address concerns associated with simultaneity bias, we demonstrate that pre‐stock‐market‐open Twitter activity preempts post‐stock‐market‐open returns. Finally, we conduct post hoc analyses that explore how firms can leverage Twitter to counteract the negative stock market consequences of Twitter, and how firm proximity to the end customer may influence how tweets moderate the stock market reaction to glitches.

Automated Code Inspection of Twitter Data using Software Repository Mining

The project proposes an application that reviews and analysis tweets on twitter application by doing software repository mining on the information gathered. The main purpose of this project is to investigate a few computational strategies to estimate the effect of web based life. Propelled by the techniques recently created to break down software systems and other unique frameworks, these strategies measure different static and dynamic parts of interpersonal organizations, the possibility and advantages of these estimation techniques with regards to Twitter is shown. By investigating the tweets the connection between the imperativeness of the news and the volume of the related tweets can be seen, which gets refreshed after every constant period of time. Using this strategy the tweets are ranked according to the highest priority.

Russian Twitter Accounts and the Partisan Polarization of Vaccine Discourse, 2015-2017.

Objectives. To understand how Twitter accounts operated by the Russian Internet Research Agency (IRA) discussed vaccines to increase the credibility of their manufactured personas.Methods. We analyzed 2.82 million tweets published by 2689 IRA accounts between 2015 and 2017. Combining unsupervised machine learning and network analysis to identify "thematic personas" (i.e., accounts that consistently share the same topics), we analyzed the ways in which each discussed vaccines.Results. We found differences in volume and valence of vaccine-related tweets among 9 thematic personas. Pro-Trump personas were more likely to express antivaccine sentiment. Anti-Trump personas expressed support for vaccination. Others offered a balanced valence, talked about vaccines neutrally, or did not tweet about vaccines.Conclusions. IRA-operated accounts discussed vaccines in manners consistent with fabricated US identities.Public Health Implications. IRA accounts discussed vaccines online in ways that evoked political identities. This could exacerbate recently emerging partisan gaps relating to vaccine misinformation, as differently valenced messages were targeted at different segments of the US public. These sophisticated targeting efforts, if repeated and increased in reach, could reduce vaccination rates and magnify health disparities.

Emotional communication analysis of emergency microblog based on the evolution life cycle of public opinion

PurposeMicroblogging is an important channel used to disseminate online public opinion during an emergency. Analyzing the features and evolution mechanism of online public opinion during an emergency plays a significant role in crisis management.Design/methodology/approachThis paper uses the event of Hurricane Irma and combines it with the life cycle of online public opinion evolution to understand the effect of different types of emotional (joy, anger, sadness, fear, disgust) microblogs (tweets) on information dissemination. The research was performed in the context of Hurricane Irma by using tweets associated with that event.FindingsThis paper demonstrates that negative emotional information has a greater communication effect, and further, the target audience that receives more exposure to negative emotional microblogs has a stronger tendency to retweet. Meanwhile, emotions expressed in tweets and the life cycle of public opinion evolution exert interactive effects on the retweeting behavior of the target audience.Research limitations/implicationsFor future research, a professional dictionary and the context should be taken into consideration to make the modeling in the text more normative and analyzable.Practical implicationsThis paper aims to reveal how the emotions of a tweet affect its virality in terms of diffusion volume in the context of an emergency event.Social implicationsThe conclusion made in this paper can shed light on the real-time regulation and public opinion transmission, as well as for efficient intelligence service and emergency management.Originality/valueIn this study, Hurricane Irma is taken as an example to explore the factors influencing the information dissemination during emergencies on the social media environment. The relationship between the sentiment of a tweet and the life cycle of public opinion and its effect on tweet volume were investigated.

What Can Twitter Tell Us about Skin Cancer Communication and Prevention on Social Media?

Background: Skin cancer places a substantial burden on the health system in Australia. The modernisation of skin cancer prevention interventions by using social media may enhance their contemporary reach and relevance, especially among the youth. This study aimed to analyse Twitter posts in Australia to establish a baseline of skin cancer and sun-related communication trends. Methods: Tweets posted over the summer of 2018/2019 relating to relevant keywords and hashtags such as “slipslopslap” and “sunscreen” were analysed using quantitative and qualitative methods. The number of tweets, related communication patterns, and possible factors for sudden changes in tweet volume were studied. Results: Results showed that Australians are using Twitter to communicate about their sun-related and skin cancer experiences and to share advice and information on this matter. Overall, Australians use Twitter more frequently to talk about sunscreen (number of tweets between December 2018 and February 2019 = 5,842) and/or skin cancer (n = 3,936), but not so often to communicate about other sun-protective behaviours (n = 1,972) or skin cancer prevention campaigns (n = 108). The number of tweets is greatly affected by the increase in temperature. Celebrities, non-health-related organisations with a high number of followers, and individuals sharing their own skin cancer experiences achieve a substantial number of likes and retweets and thus influence on Twitter regarding skin cancer-related communication. Conclusions: The results allow deeper understanding of important determinants for effective social media use for health promotion and public health messaging in the future. Linking tweets with temperature, sharing by celebrities or non-health-related organisations and individuals with a high following can all contribute to greater spread of skin cancer and sun-related messages.