Mass Media and the Contagion of Fear: The Case of Ebola in America

Background

In the weeks following the first imported case of Ebola in the U. S. on September 29, 2014, coverage of the very limited outbreak dominated the news media, in a manner quite disproportionate to the actual threat to national public health; by the end of October, 2014, there were only four laboratory confirmed cases of Ebola in the entire nation. Public interest in these events was high, as reflected in the millions of Ebola-related Internet searches and tweets performed in the month following the first confirmed case. Use of trending Internet searches and tweets has been proposed in the past for real-time prediction of outbreaks (a field referred to as “digital epidemiology”), but accounting for the biases of public panic has been problematic. In the case of the limited U. S. Ebola outbreak, we know that the Ebola-related searches and tweets originating the U. S. during the outbreak were due only to public interest or panic, providing an unprecedented means to determine how these dynamics affect such data, and how news media may be driving these trends.

Methodology

We examine daily Ebola-related Internet search and Twitter data in the U. S. during the six week period ending Oct 31, 2014. TV news coverage data were obtained from the daily number of Ebola-related news videos appearing on two major news networks. We fit the parameters of a mathematical contagion model to the data to determine if the news coverage was a significant factor in the temporal patterns in Ebola-related Internet and Twitter data.

Conclusions

We find significant evidence of contagion, with each Ebola-related news video inspiring tens of thousands of Ebola-related tweets and Internet searches. Between 65% to 76% of the variance in all samples is described by the news media contagion model.     

Aging 2.0: Health Information about Dementia on Twitter

Online social media is widespread, easily accessible and attracts a global audience with a widening demographic. As a large proportion of adults now seek health information online and through social media applications, communication about health has become increasingly interactive and dynamic. Online health information has the potential to significantly impact public health, especially as the population gets older and the prevalence of dementia increases. However, little is known about how information pertaining to age-associated diseases is disseminated on popular social media platforms. To fill this knowledge gap, we examined empirically: (i) who is using social media to share information about dementia, (ii) what sources of information about dementia are promoted, and (iii) which dementia themes dominate the discussion. We data-mined the microblogging platform Twitter for content containing dementia-related keywords for a period of 24 hours and retrieved over 9,200 tweets. A coding guide was developed and content analysis conducted on a random sample (10%), and on a subsample from top users’ tweets to assess impact. We found that a majority of tweets contained a link to a third party site rather than personal information, and these links redirected mainly to news sites and health information sites. As well, a large number of tweets discussed recent research findings related to the prediction and risk management of Alzheimer’s disease. The results highlight the need for the dementia research community to harness the reach of this medium and its potential as a tool for multidirectional engagement.     

Tweeting Supertyphoon Haiyan: Evolving Functions of Twitter during and after a Disaster Event

When disaster events capture global attention users of Twitter form transient interest communities that disseminate information and other messages online. This paper examines content related to Typhoon Haiyan (locally known as Yolanda) as it hit the Philippines and triggered international humanitarian response and media attention. It reveals how Twitter conversations about disasters evolve over time, showing an issue attention cycle on a social media platform. The paper examines different functions of Twitter and the information hubs that drive and sustain conversation about the event. Content analysis shows that the majority of tweets contain information about the typhoon or its damage, and disaster relief activities. There are differences in types of content between the most retweeted messages and posts that are original tweets. Original tweets are more likely to come from ordinary users, who are more likely to tweet emotions, messages of support, and political content compared with official sources and key information hubs that include news organizations, aid organization, and celebrities. Original tweets reveal use of the site beyond information to relief coordination and response.     

Concurrent Bursty Behavior of Social Sensors in Sporting Events

The advent of social media expands our ability to transmit information and connect with others instantly, which enables us to behave as “social sensors.” Here, we studied concurrent bursty behavior of Twitter users during major sporting events to determine their function as social sensors. We show that the degree of concurrent bursts in tweets (posts) and retweets (re-posts) works as a strong indicator of winning or losing a game. More specifically, our simple tweet analysis of Japanese professional baseball games in 2013 revealed that social sensors can immediately react to positive and negative events through bursts of tweets, but that positive events are more likely to induce a subsequent burst of retweets. We confirm that these findings also hold true for tweets related to Major League Baseball games in 2015. Furthermore, we demonstrate active interactions among social sensors by constructing retweet networks during a baseball game. The resulting networks commonly exhibited user clusters depending on the baseball team, with a scale-free connectedness that is indicative of a substantial difference in user popularity as an information source. While previous studies have mainly focused on bursts of tweets as a simple indicator of a real-world event, the temporal correlation between tweets and retweets implies unique aspects of social sensors, offering new insights into human behavior in a highly connected world.     

Ferguson and the death of Michael Brown on Twitter: #BlackLivesMatter, #TCOT,and the evolution of collective identities

Recent events related to police brutality and the evolution of #BlackLivesMatter provides an empirical case to explore the vitality of social media data for social movements and the evolution of collective identities. Social media data provide a portal into how organizing and communicating generate narratives that survive over time. We analyse 31.65 million tweets about Ferguson across four meaningful time periods: the death of Michael Brown, the non-indictment of police officer Darren Wilson, the Department of Justice report on Ferguson, and the one year aftermath of Brown’s death. Our analysis shows that #BlackLivesMatter evolved in concert with protests opposing police brutality occurring on the ground. We also show how #TCOT (Top Conservatives on Twitter) has operated as the primary counter narrative to #BlackLivesMatter. We conclude by discussing the implications our research has for the #BlackLivesMatter movement and increased political polarization following the election of Donald Trump.     

Tweets on the Road

The pervasiveness of mobile devices, which is increasing daily, is generating a vast amount of geo-located data allowing us to gain further insights into human behaviors. In particular, this new technology enables users to communicate through mobile social media applications, such as Twitter, anytime and anywhere. Thus, geo-located tweets offer the possibility to carry out in-depth studies on human mobility. In this paper, we study the use of Twitter in transportation by identifying tweets posted from roads and rails in Europe between September 2012 and November 2013. We compute the percentage of highway and railway segments covered by tweets in 39 countries. The coverages are very different from country to country and their variability can be partially explained by differences in Twitter penetration rates. Still, some of these differences might be related to cultural factors regarding mobility habits and interacting socially online. Analyzing particular road sectors, our results show a positive correlation between the number of tweets on the road and the Average Annual Daily Traffic on highways in France and in the UK. Transport modality can be studied with these data as well, for which we discover very heterogeneous usage patterns across the continent.     

The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic

Twitter is a free social networking and micro-blogging service that enables its millions of users to send and read each other's "tweets," or short, 140-character messages. The service has more than 190 million registered users and processes about 55 million tweets per day. Useful information about news and geopolitical events lies embedded in the Twitter stream, which embodies, in the aggregate, Twitter users' perspectives and reactions to current events. By virtue of sheer volume, content embedded in the Twitter stream may be useful for tracking or even forecasting behavior if it can be extracted in an efficient manner. In this study, we examine the use of information embedded in the Twitter stream to (1) track rapidly-evolving public sentiment with respect to H1N1 or swine flu, and (2) track and measure actual disease activity. We also show that Twitter can be used as a measure of public interest or concern about health-related events. Our results show that estimates of influenza-like illness derived from Twitter chatter accurately track reported disease levels.     

He Votes or She Votes? Female and Male Discursive Strategies in Twitter Political Hashtags

In this paper, we conduct a study about differences between female and male discursive strategies when posting in the microblogging service Twitter, with a particular focus on the hashtag designation process during political debate. The fact that men and women use language in distinct ways, reverberating practices linked to their expected roles in the social groups, is a linguistic phenomenon known to happen in several cultures and that can now be studied on the Web and on online social networks in a large scale enabled by computing power. Here, for instance, after analyzing tweets with political content posted during Brazilian presidential campaign,we found out that male Twitter users, when expressing their attitude toward a given candidate, are more prone to use imperative verbal forms in hashtags, while female users tend to employ declarative forms. This difference can be interpreted as a sign of distinct approaches in relation to other network members: for example, if political hashtags are seen as strategies of persuasion in Twitter, imperative tags could be understood as more overt ways of persuading and declarative tags as more indirect ones. Our findings help to understand human gendered behavior in social networks and contribute to research on the new fields of computer-enabled Internet linguistics and social computing, besides being useful for several computational tasks such as developing tag recommendation systems based on users'' collective preferences and tailoring targeted advertising strategies, among others.     

Uncovering Spatiotemporal Characteristics of Human Online Behaviors during Extreme Events

In response to an extreme event, individuals on social media demonstrate interesting behaviors depending on their backgrounds. By making use of the large-scale datasets of posts and search queries collected from Twitter and GoogleTrends, we first identify the distinct categories of human collective online concerns and durations based on the distributions of solo tweets and new incremental tweets about events. Such a characterization enables us to gain a better understanding of dynamic changes in human behaviors corresponding to different types of events. Next, we observe the heterogeneity of individual responses to events through measuring the fraction of event-related tweets relative to the tweets released by an individual, and thus empirically confirm the heterogeneity assumption as adopted in the meta-population models for characterizing collective responses to events. Finally, based on the correlations of information entropy in different regions, we show that the observed distinct responses may be caused by their different speeds in information propagation. In addition, based on the detrended fluctuation analysis, we find that there exists a self-similar evolution process for the collective responses within a region. These findings have provided a detailed account for the nature of distinct human behaviors on social media in presence of extreme events.     

Quantifying Collective Attention from Tweet Stream

Online social media are increasingly facilitating our social interactions, thereby making available a massive “digital fossil” of human behavior. Discovering and quantifying distinct patterns using these data is important for studying social behavior, although the rapid time-variant nature and large volumes of these data make this task difficult and challenging. In this study, we focused on the emergence of “collective attention” on Twitter, a popular social networking service. We propose a simple method for detecting and measuring the collective attention evoked by various types of events. This method exploits the fact that tweeting activity exhibits a burst-like increase and an irregular oscillation when a particular real-world event occurs; otherwise, it follows regular circadian rhythms. The difference between regular and irregular states in the tweet stream was measured using the Jensen-Shannon divergence, which corresponds to the intensity of collective attention. We then associated irregular incidents with their corresponding events that attracted the attention and elicited responses from large numbers of people, based on the popularity and the enhancement of key terms in posted messages or “tweets.” Next, we demonstrate the effectiveness of this method using a large dataset that contained approximately 490 million Japanese tweets by over 400,000 users, in which we identified 60 cases of collective attentions, including one related to the Tohoku-oki earthquake. “Retweet” networks were also investigated to understand collective attention in terms of social interactions. This simple method provides a retrospective summary of collective attention, thereby contributing to the fundamental understanding of social behavior in the digital era.     

Quantifying Discrepancies in Opinion Spectra from Online and Offline Networks

Online social media such as Twitter are widely used for mining public opinions and sentiments on various issues and topics. The sheer volume of the data generated and the eager adoption by the online-savvy public are helping to raise the profile of online media as a convenient source of news and public opinions on social and political issues as well. Due to the uncontrollable biases in the population who heavily use the media, however, it is often difficult to measure how accurately the online sphere reflects the offline world at large, undermining the usefulness of online media. One way of identifying and overcoming the online–offline discrepancies is to apply a common analytical and modeling framework to comparable data sets from online and offline sources and cross-analyzing the patterns found therein. In this paper we study the political spectra constructed from Twitter and from legislators'' voting records as an example to demonstrate the potential limits of online media as the source for accurate public opinion mining, and how to overcome the limits by using offline data simultaneously.     

Social Media,Public Participation,and the 2010 BP Deepwater Horizon Oil Spill

This research examines how information about an oil spill, its impacts, and the use of dispersants to treat the oil, moved through social media and the surrounding Internet during the 2010 BP Deepwater Horizon oil spill. Using a collection of tweets captured during the spill, we employ a mixed-method approach including an in-depth qualitative analysis to examine the content of Twitter posts, the connections that Twitter users made with each other, and the links between Twitter content and the surrounding Internet. This article offers a range of findings to help practitioners and others understand how social media is used by a variety of different actors during a slow-moving, long-term, environmental disaster. We enumerate some of the most salient themes in the Twitter data, noting that concerns about health impacts were more likely to be communicated in tweets about dispersant use, than in the larger conversation. We describe the accounts and behaviors of highly retweeted Twitter users, noting how locals helped to shape the network and the conversation. Importantly, our results show the online crowd wanting to participate in and contribute to response efforts, a finding with implications for future oil spill response.     

National and Local Influenza Surveillance through Twitter: An Analysis of the 2012-2013 Influenza Epidemic

Social media have been proposed as a data source for influenza surveillance because they have the potential to offer real-time access to millions of short, geographically localized messages containing information regarding personal well-being. However, accuracy of social media surveillance systems declines with media attention because media attention increases “chatter” – messages that are about influenza but that do not pertain to an actual infection – masking signs of true influenza prevalence. This paper summarizes our recently developed influenza infection detection algorithm that automatically distinguishes relevant tweets from other chatter, and we describe our current influenza surveillance system which was actively deployed during the full 2012-2013 influenza season. Our objective was to analyze the performance of this system during the most recent 2012–2013 influenza season and to analyze the performance at multiple levels of geographic granularity, unlike past studies that focused on national or regional surveillance. Our system’s influenza prevalence estimates were strongly correlated with surveillance data from the Centers for Disease Control and Prevention for the United States (r = 0.93, p < 0.001) as well as surveillance data from the Department of Health and Mental Hygiene of New York City (r = 0.88, p < 0.001). Our system detected the weekly change in direction (increasing or decreasing) of influenza prevalence with 85% accuracy, a nearly twofold increase over a simpler model, demonstrating the utility of explicitly distinguishing infection tweets from other chatter.     

#SayHerName: a case study of intersectional social media activism

Social media activism presents sociologists with the opportunity to develop a deeper understanding of how groups form and sustain collective identities around political issues throughout the course of a social movement. This paper contributes to a growing body of sociological literature on social media by applying an intersectional framework to a content analysis of over 400,000 tweets related to #SayHerName. Our findings demonstrate that Twitter users who identified with #SayHerName engage in intersectional mobilization by highlighting Black women victims of police violence and giving attention to intersections with gender identity. #SayHerName is a dialogue that centres Black cisgender and transgender women victims of state-sanctioned violence. Additionally, #SayHerName is a space for highlighting Black women victims of non-police violence. Therefore, we propose that future research on social media activism should incorporate intersectionality as a basis for understanding the symbols and language of twenty-first century social movements.     

Western Balkan societies' awareness of air pollution. Estimations using natural language processing techniques

Air pollution remains a severe concern in European countries, especially in Western Balkan, where the air monitoring data point to harmful ambient pollution. The public concern with this issue becomes particularly critical during the fall and winter months, when the contamination is more visible, provoking a series of reactions directed principally to the government authorities as the responsible entities for regulating air pollution levels. Since citizen-contributed data are generally considered valuable additional information for assessing the impacts of air pollution, the public contribution could act as a tool for increasing awareness and response about air pollution. Consequently, this study's objective focuses on researching public awareness of air pollution in Western Balkan. The study assumes that citizens' reactions will grow more intensely during the months with an increase in air pollution levels, principally due to winter heating. Therefore, Twitter activity and news articles related to air pollution have been investigated for the case of Macedonia, Serbia, Bosnia and Herzegovina and Montenegro, from November 2021 to March 2022. Natural Language Processing techniques such as sentiment analysis, topic modelling, and cross-correlations statistical analysis were employed to determine the relationship between Twitter discussions and news with actual PM₁₀ levels measured by official air monitoring stations. The aim was to observe whether tweets and news teasers reflect the realistic air pollution situation. The results affirm that social media discussions, mainly with a negative connotation, can serve as a measure of public awareness of temporal changes in the PM₁₀ concentration in the air and the negative consequences. The content of the resources reveals several topics of concern, contributing to better identification of public opinion and possibilities for tracking news trends. Nevertheless, attention should be paid to news interpretation, provided that sometimes they might offer a more neutral understanding of the situation, failing, in this way, to present the actual air conditions and possibly impacting society in forming an unrealistic opinion. Additionally, the public might not be able to obtain sufficient or accurate information about the primary sources of air pollution, emphasizing the need for more transparent communication and greater education regarding air pollution monitoring. Finally, the study provides deeper insights into the content of the data and helps detect the reasons for skepticism towards pro-environmental behavior occurring in social media discussions. Explicitly, personal disappointment with the air quality should be taken as an inflection point by responsible parties to intervene in improving citizens' quality of life.     

Life Satisfaction and the Pursuit of Happiness on Twitter

Life satisfaction refers to a somewhat stable cognitive assessment of one’s own life. Life satisfaction is an important component of subjective well being, the scientific term for happiness. The other component is affect: the balance between the presence of positive and negative emotions in daily life. While affect has been studied using social media datasets (particularly from Twitter), life satisfaction has received little to no attention. Here, we examine trends in posts about life satisfaction from a two-year sample of Twitter data. We apply a surveillance methodology to extract expressions of both satisfaction and dissatisfaction with life. A noteworthy result is that consistent with their definitions trends in life satisfaction posts are immune to external events (political, seasonal etc.) unlike affect trends reported by previous researchers. Comparing users we find differences between satisfied and dissatisfied users in several linguistic, psychosocial and other features. For example the latter post more tweets expressing anger, anxiety, depression, sadness and on death. We also study users who change their status over time from satisfied with life to dissatisfied or vice versa. Noteworthy is that the psychosocial tweet features of users who change from satisfied to dissatisfied are quite different from those who stay satisfied over time. Overall, the observations we make are consistent with intuition and consistent with observations in the social science research. This research contributes to the study of the subjective well being of individuals through social media.     

Do bedroom eyes wear political glasses? The role of politics in human mate attraction

Most social science research portrays attitudes and behaviors as a product of one's environment or social upbringing. Recently, however, scholars have begun to expand upon this paradigm by showing that biological factors such as genes, which are passed from parents to offspring, can also help explain differences in political attitudes and behaviors. As a result, illuminating how spouses select one another is the first step toward understanding both the genetic and social transmission of political preferences from parents to offspring. Yet the question of whether individuals actively seek out mates who are more politically similar is unknown. To address this lacuna, data were gathered from Internet dating profiles. These data show that most individuals are reluctant to advertise politics when attempting to attract a mate. However, the correlates of political attitudes and behavior, such as education and civic engagement, do predict whether a person uses politics as a way to attract a mate. Thus, although spouses share such predilections more than almost any other trait, individuals do not appear to initially select potential dates along political lines.     

From Healing to Witchcraft: On Ritual Speech and Roboticization in the Hospital

Healthcare Information Technology (HIT), touted as a panacea by U.S. political actors ranging from Newt Gingrich to Barack Obama, is central to emerging forms of healthcare governance which Holmes et al.—in their critique of the institutionalization of magical thinking brought about by Orwellian techno-Newspeak—have provocatively labeled fascistic. Drawing from data collected over 3 years of working with and teaching continuing education (CE) courses for thousands of registered nurses as lead political educator for the California Nurses Association/National Nurses Organizing Committee (CNA/NNOC), I argue that HIT is an integral component of a broader technological restructuring of healthcare and thus society, both of which are part of a social discourse that is tied to a transformative system of ritual speech, with profound implications for healthcare work, patient health, and democracy.     

The Mental Representation of Social Connections: Generalizability Extended to Beijing Adults

Social connections are essential for the survival of a social species like humans. People differ in the degree to which they are sensitive to perceived deficits in their social connections, but evidence suggests that they nevertheless construe the nature of their social connections similarly. This construal can be thought of as a mental representation of a multi-faceted social experience. A three-dimensional mental representation has been identified with the UCLA Loneliness Scale and consists of Intimate, Relational, and Collective Connectedness reflecting beliefs about one''s individual, dyadic, and collective (group) social value, respectively. Moreover, this mental representation has been replicated with other scales and validated across age, gender, and racial/ethnic lines in U.S. samples. The purpose of this study is to evaluate the extent to which this three-dimensional representation applies to people whose social lives are experienced in a collectivistic rather than individualistic culture. To that end, we used confirmatory factor analyses to assess the fit of the three-dimensional mental structure to data collected from Chinese people living in China. Two hundred sixty-seven young adults (16–25 yrs) and 250 older adults (50–65 yrs) in Beijing completed the revised UCLA Loneliness Scale and demographic and social activity questionnaires. Results revealed adequate fit of the structure to data from young and older Chinese adults. Moreover, the structure exhibited equivalent fit in young and older Chinese adults despite changes in the Chinese culture that exposed these two generations to different cultural experiences. Social activity variables that discriminated among the three dimensions in the Chinese samples corresponded well with variables that discriminated among the three dimensions in the U.S.-based samples, indicating cultural commonalities in the factors predicting dimensions of people''s representations of their social connections. Equivalence of the three-dimensional structure is relevant for an understanding of cultural differences in the sources of loneliness and social connectedness.     

Discovering Social Events through Online Attention

Twitter is a major social media platform in which users send and read messages (“tweets”) of up to 140 characters. In recent years this communication medium has been used by those affected by crises to organize demonstrations or find relief. Because traffic on this media platform is extremely heavy, with hundreds of millions of tweets sent every day, it is difficult to differentiate between times of turmoil and times of typical discussion. In this work we present a new approach to addressing this problem. We first assess several possible “thermostats” of activity on social media for their effectiveness in finding important time periods. We compare methods commonly found in the literature with a method from economics. By combining methods from computational social science with methods from economics, we introduce an approach that can effectively locate crisis events in the mountains of data generated on Twitter. We demonstrate the strength of this method by using it to locate the social events relating to the Occupy Wall Street movement protests at the end of 2011.