- Sociology, Journalism, Research Methodology, Social Media, Qualitative methodology, Digital Culture, and 13 moreInteractive and Digital Media, South Asia, Ethnography (Research Methodology), Big Data, Virtual Ethnography, Virtual Teams, Organizations, Quantitative Sociology, Digital Media, Media, Social Network Analysis (SNA), Globalization, and Social Networkingedit
- Dhiraj Murthy is a Professor of Journalism and Sociology at the University of Texas at Austin. He was previously Read... moreDhiraj Murthy is a Professor of Journalism and Sociology at the University of Texas at Austin. He was previously Reader of Sociology at Goldsmiths, University of London. His research explores social media, digital research methods, race/ethnicity, qualitative/mixed methods, big data quantitative analysis, and virtual organizations. Dr. Murthy has authored over 70 articles, book chapters, and papers and a book about Twitter, the first on the subject (published by Polity Press, 2013 and fully revised in 2018). He was been funded by the National Science Foundation, National Institutes of Health, and various international/national grants. Dr. Murthy founded and directs the Computational Media Lab at UT Austin.edit
Long-standing results in urban studies have shown correlation of population and population density to a city’s pace of life, empirically tested by examining whether individuals in bigger cities walk faster, spend less time buying stamps,... more
Long-standing results in urban studies have shown correlation of population and population density to a city’s pace of life, empirically tested by examining whether individuals in bigger cities walk faster, spend less time buying stamps, or make greater numbers of telephone calls. Contemporary social media presents a new opportunity to test these hypotheses. This study examines whether users of the social media platform Twitter in larger and denser American cities tweet at a faster rate than their counterparts in smaller and sparser ones. Contrary to how telephony usage and productivity scale superlinearly with city population, the total volume of tweets in cities scales sublinearly. This is similar to the economies of scale in city infrastructures like gas stations. When looking at individuals, however, greater population density is associated with faster tweeting. The discrepancy between the ecological correlation and individual behavior is resolved by noting that larger cities ha...
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This article seeks to extend social science scholarship on social media technology use during disruptive events. Though social media's role in times of crisis has been previously studied, much of this work tends to... more
This article seeks to extend social science scholarship on social media technology use during disruptive events. Though social media's role in times of crisis has been previously studied, much of this work tends to focus on first-responders and relief organizations. However, social media use during disasters tends to be decentralized and this organizational structure can promote different types of messages to top-down information systems. Using 142,786 geo-tagged tweets collected before and after Hurricane Sandy's US landfall as a case study, this article seeks to explore shifts in social media behavior during disruptive events and highlights that though Sandy disrupted routine life within Twitter, users responded to the disaster by employing humor, sharing photos, and checking into locations. We conclude that social media use during disruptive events is complex and understanding these nuanced behaviors is important across the social sciences.
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Research Interests: Sociology, Demography, Computer Science, Communication, Race and Ethnicity, and 11 moreUrban Studies, Social Media, Computer Mediated Communication, Twitter, Urban Sociology, Ethnicity, Political Mobilization, Demographics, Language Culture and Communication, Social Networking & Social Media, and BlackLivesMatter
This chapter reviews existing data mining tools for scraping data from heterogeneous online social networks. It introduces not only the complexities of scraping data from these sources (which include diverse data forms), but also presents... more
This chapter reviews existing data mining tools for scraping data from heterogeneous online social networks. It introduces not only the complexities of scraping data from these sources (which include diverse data forms), but also presents currently available tools including their strengths and weaknesses. The chapter introduces our solution to effectively mining online social networks through the development of VoyeurServer, a tool we designed which builds upon the open-source Web-Harvest framework. We have shared details of how VoyeurServer was developed and how it works so that data mining developers can develop their own customized data mining solutions built upon the Web-Harvest framework. We conclude the chapter with future directions of our data mining project so that developers can incorporate relevant features into their data mining applications.
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Cancer patients, family members and friends are increasingly using social media. Some oncologists and oncology centres are engaging with social media, and advocacy groups are using it to disseminate information and coordinate fundraising... more
Cancer patients, family members and friends are increasingly using social media. Some oncologists and oncology centres are engaging with social media, and advocacy groups are using it to disseminate information and coordinate fundraising efforts. However, the question of whether such social media activity corresponds to areas with higher incidence of cancer or higher access to cancer centres remains understudied. To address this gap, our study compared US government data with 90,986 cancer-related tweets with the keywords ‘chemo’, ‘lymphoma’, ‘mammogram’, ‘melanoma’, and ‘cancer survivor’. We found that the frequency of cancer-related tweets is not associated with mammogram testing and cancer incidence rates, but that the concentration of doctors and cancer centres is associated with cancer-related tweet frequency. Ultimately, we found that Twitter has value to cancer patients, survivors and their families, but that cancer-related social media resources may not be targeting location...
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Background The volume of COVID-19–related misinformation has long exceeded the resources available to fact checkers to effectively mitigate its ill effects. Automated and web-based approaches can provide effective deterrents to online... more
Background The volume of COVID-19–related misinformation has long exceeded the resources available to fact checkers to effectively mitigate its ill effects. Automated and web-based approaches can provide effective deterrents to online misinformation. Machine learning–based methods have achieved robust performance on text classification tasks, including potentially low-quality-news credibility assessment. Despite the progress of initial, rapid interventions, the enormity of COVID-19–related misinformation continues to overwhelm fact checkers. Therefore, improvement in automated and machine-learned methods for an infodemic response is urgently needed. Objective The aim of this study was to achieve improvement in automated and machine-learned methods for an infodemic response. Methods We evaluated three strategies for training a machine-learning model to determine the highest model performance: (1) COVID-19–related fact-checked data only, (2) general fact-checked data only, and (3) com...
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When wide-scale flooding occurs in a community not accustomed to floods, health concerns emerge. While official organizations tasked with communicating emerging health information exist, the proliferation of social media makes it possible... more
When wide-scale flooding occurs in a community not accustomed to floods, health concerns emerge. While official organizations tasked with communicating emerging health information exist, the proliferation of social media makes it possible for average citizens to participate in this conversation. This study used a combination of semi-structured interviews and photo elicitation techniques to explore how citizens used private social media sites to share health information. We found two main categories of health concerns: existing medical conditions and water-created. We further identified six themes that describe the common approaches average citizens used to share health information: Narrating a personal experience, presenting it as a Public Service Announcement, downplaying the contribution, bringing a credible source into the conversation, including external links and sources, and using humor. Together, these findings suggest that citizens need health information during a flood disa...
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When natural disasters occur, various organizations and agencies turn to social media to understand who needs help and how they have been affected. The purpose of this study is twofold: first, to evaluate whether hurricane-related tweets... more
When natural disasters occur, various organizations and agencies turn to social media to understand who needs help and how they have been affected. The purpose of this study is twofold: first, to evaluate whether hurricane-related tweets have some consistency over time, and second, whether Twitter-derived content is thematically similar to other private social media data. Through a unique method of using Twitter data gathered from six different hurricanes, alongside private data collected from qualitative interviews conducted in the immediate aftermath of Hurricane Harvey, we hypothesize that there is some level of stability across hurricane-related tweet content over time that could be used for better real-time processing of social media data during natural disasters. We use latent Dirichlet allocation (LDA) to derive topics, and, using Hellinger distance as a metric, find that there is a detectable connection among hurricane topics. By uncovering some persistent thematic areas and...
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Widespread disasters can overload official agencies’ capacity to provide assistance, and often citizen-led groups emerge to assist with disaster response. As social media platforms have expanded, emergent rescue groups have many ways to... more
Widespread disasters can overload official agencies’ capacity to provide assistance, and often citizen-led groups emerge to assist with disaster response. As social media platforms have expanded, emergent rescue groups have many ways to harness network and mobile tools to coordinate actions and help fellow citizens. This study used semistructured interviews and photo elicitation techniques to better understand how wide-scale rescues occurred during the 2017 Hurricane Harvey flooding in the Greater Houston, Texas USA area. We found that citizens used diverse apps and social media-related platforms during these rescues and that they played one of three roles: rescuer, dispatcher, or information compiler. The key social media coordination challenges these rescuers faced were incomplete feedback loops, unclear prioritization, and communication overload. This work-in-progress paper contributes to the field of crisis and disaster response research by sharing the nuances in how citizens us...
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Twitter gained new levels of political prominence with Donald J. Trump’s use of the platform. Although previous work has been done studying the content of Trump’s tweets, there remains a dearth of research exploring who opinion leaders... more
Twitter gained new levels of political prominence with Donald J. Trump’s use of the platform. Although previous work has been done studying the content of Trump’s tweets, there remains a dearth of research exploring who opinion leaders were in the early days of his presidency and what they were tweeting about. Therefore, this study retroactively investigates opinion leaders on Twitter during Trump’s 1st month in office and explores what those influencers tweeted about. We uniquely used a historical data set of 3 million tweets that contained the word “trump” and used Latent Dirichlet Allocation, a probabilistic algorithmic model, to extract topics from both general Twitter users and opinion leaders. Opinion leaders were identified by measuring eigenvector centrality and removing users with fewer than 10,000 followers. The top 1% users with the highest score in eigencentrality ( N = 303) were sampled, and their attributes were manually coded. We found that most Twitter-based opinion ...
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As social media technologies such as Twitter, Instagram, and YouTube have become highly ubiquitous, social life itself has become reconfigured. Though early notions of an offline/online binary remain in some quarters of social research,... more
As social media technologies such as Twitter, Instagram, and YouTube have become highly ubiquitous, social life itself has become reconfigured. Though early notions of an offline/online binary remain in some quarters of social research, there is a realization amongst most that this binary is reified. As such, the study of social interactions within social media is a fundamental sociological question. This chapter argues that social researchers need to engage with the study of social media in order to comprehensively understand modern social life. This chapter also provides insights into how we, as social researchers, can critically collect and discern social formations via social media. Twitter is specifically used in this chapter to provide an example of how the medium provides opportunities for mixed qualitative and quantitative social analysis. Ultimately, this chapter also argues that the understanding of large social questions is increasingly contingent on us deciphering and understanding social knowledge formed and maintained within social media platforms.
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Social media have become increasingly pervasive. However, the literature on social movements and social media has not fully grasped just how much social media have fundamentally changed the landscape of organizational communication,... more
Social media have become increasingly pervasive. However, the literature on social movements and social media has not fully grasped just how much social media have fundamentally changed the landscape of organizational communication, ranging from stakeholders being able to directly mobilize resources to making grassroots transnational social movements more organizationally feasible. A major gap in the literature is this lack of understanding how social media have shaped social movement organizations (SMOs) and the organization of social movements. This Special Issue brings together a unique collection of articles that map and comment on the field of social media and social movements. The volume contributes to literature in this area by exploring how social media are not only shaping social movements, advocacy, and activism from the point of view of organizational communication but also changing the ways in which activists and SMOs interact with each other. The volume leverages a dive...
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This paper explores a variety of methods for applying the Latent Dirichlet Allocation (LDA) automated topic modeling algorithm to the modeling of the structure and behavior of virtual organizations found within modern social media and... more
This paper explores a variety of methods for applying the Latent Dirichlet Allocation (LDA) automated topic modeling algorithm to the modeling of the structure and behavior of virtual organizations found within modern social media and social networking environments. As the field of Big Data reveals, an increase in the scale of social data available presents new challenges which are not tackled by merely scaling up hardware and software. Rather, they necessitate new methods and, indeed, new areas of expertise. Natural language processing provides one such method. This paper applies LDA to the study of scientific virtual organizations whose members employ social technologies. Because of the vast data footprint in these virtual platforms, we found that natural language processing was needed to 'unlock' and render visible latent, previously unseen conversational connections across large textual corpora (spanning profiles, discussion threads, forums, and other social media incarn...
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This research develops a model of mobile social network dispersion in rescue communication, and illustrates how people use a combination of mobile and social media, along with real-time communication, in their decision-making process.... more
This research develops a model of mobile social network dispersion in rescue communication, and illustrates how people use a combination of mobile and social media, along with real-time communication, in their decision-making process. Guided by established research on smartphones, social media, and affordances, we used a qualitative approach and conducted field interviews that included photo-elicitation interview (PEI) techniques to examine participants’ private social media data. Our analysis of these rescue decisions reveals why so few people used the official 9-1-1 system. We show how rescue communication often occurs through a socially constructed assessment of risk that involves persuasion by trusted others in their network, regardless of professional qualifications. Furthermore, trusted others can function as proxies and can draw upon mobile social network affordances, helping to compensate for material limitations. The affordances people drew from can be organized into two se...
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Building a benchmark dataset for hate speech detection presents several challenges. Firstly, because hate speech is relatively rare – e.g., less than 3% of Twitter posts are hateful [14] – random sampling of tweets to annotate is... more
Building a benchmark dataset for hate speech detection presents several challenges. Firstly, because hate speech is relatively rare – e.g., less than 3% of Twitter posts are hateful [14] – random sampling of tweets to annotate is inefficient in capturing hate speech. A common practice is to only annotate tweets containing known “hate words”, but this risks yielding a biased benchmark that only partially captures the real-world phenomenon of interest. A second challenge is that definitions of hate speech tend to be highly variable and subjective. Annotators having diverse prior notions of hate speech may not only disagree with one another but also struggle to conform to specified labeling guidelines. Our key insight is that the rarity and subjectivity of hate speech are akin to that of relevance in information retrieval (IR) [38]. This connection suggests that well-established methodologies for creating IR test collections might also be usefully applied to create better benchmark dat...
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This study aims to understand the changes in behavior over time of users on Venmo, an American social payments platform. As there is a dearth of replication studies in social media studies, we chose to replicate an existing study of Venmo... more
This study aims to understand the changes in behavior over time of users on Venmo, an American social payments platform. As there is a dearth of replication studies in social media studies, we chose to replicate an existing study of Venmo using new data we collected. This enabled us to track the growth of Venmo from the beginning of the platform, and to verify the robustness of the existing, very limited literature using Venmo data. To accomplish this, we studied how the structure of the transaction graph of Venmo transactions changed since 2016, the data endpoint of previous research. Additionally, we collected a much larger set of data and examined if new community structures or network-level features emerged since 2016. Although we found that Venmo's growth has maintained a similar pattern within its transaction graph and community structure, we discovered some changes, such as the existence of more communities of a smaller size and an increase towards users quitting the plat...
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This paper highlights the rationale for the development of BioViz, a tool to help visualize the existence of collective user interactions in online life science communities. The first community studied has approximately 22,750 unique... more
This paper highlights the rationale for the development of BioViz, a tool to help visualize the existence of collective user interactions in online life science communities. The first community studied has approximately 22,750 unique users and the second has 35,000. Making sense of the number of interactions between actors in these networks in order to discern patterns of collective organization and intelligent behavior is challenging. One of the complications is that forums - our object of interest - can vary in their purpose and remit (e.g. the role of gender in the life sciences to forums of praxis such as one exploring the cell line culturing) and this shapes the structure of the forum organization itself. Our approach took a random sample of 53 forums which were manually analyzed by our research team and interactions between actors were recorded as arcs between nodes. The paper focuses on a discussion of the utility of our approach, but presents some brief results to highlight ...
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This article examines the role of blogs during the aftermath of the 2004 Indian Ocean tsunami. Using a blog created by South Asian journalists as a case study, the article argues that new media has the potential to be a democratizing... more
This article examines the role of blogs during the aftermath of the 2004 Indian Ocean tsunami. Using a blog created by South Asian journalists as a case study, the article argues that new media has the potential to be a democratizing agent in lesser developed countries. The article argues that some tsunami-related blogs give regional, subaltern journalists a medium to transcend exploitative accounts of the tsunami’s aftermath. The article is also able to use tsunami-related blogs to help highlight questions surrounding new media and disaster reporting in lesser developed countries in general, including discussions of the digital divide.
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YouTube has traditionally been singled out as particularly influential in the spreading of ISIS content. However, the platform along with Facebook, Twitter, and Microsoft jointly created the Global Internet Forum to Counter Terrorism in... more
YouTube has traditionally been singled out as particularly influential in the spreading of ISIS content. However, the platform along with Facebook, Twitter, and Microsoft jointly created the Global Internet Forum to Counter Terrorism in 2017 as one mode to be more accountable and take measures toward combating extremist content online. Though extreme content on YouTube has been found to have decreased substantially due to this and other efforts (human and machine-based), it is valuable to historically review what role YouTube previously had in order to better understand the evolution of contemporary moves toward platform accountability in terms of extremist video content sharing. Therefore, this study explores what role YouTube’s recommender algorithm had in directing users to ISIS-related content prior to large-scale pressure by citizens and governments to more aggressively moderate extremist content. To investigate this, a YouTube video network from 2016 consisting of 15,021 video...
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This study explains how bots interact with human users and influence conversational networks on Twitter. We analyze a high-stakes political environment, the UK general election of May 2015, asking human volunteers to tweet from... more
This study explains how bots interact with human users and influence conversational networks on Twitter. We analyze a high-stakes political environment, the UK general election of May 2015, asking human volunteers to tweet from purpose-made Twitter accounts—half of which had bots attached—during three events: the last Prime Minister’s Question Time before Parliament was dissolved (#PMQs), the first leadership interviews of the campaign (#BattleForNumber10), and the BBC Question Time broadcast of the same evening (#BBCQT). Based on previous work, our expectation was that our intervention would make a significant difference to the evolving network, but we found that the bots we used had very little effect on the conversation network at all. There are economic, social, and temporal factors that impact how a user of bots can influence political conversations. Future research needs to account for these forms of capital when assessing the impact of bots on political discussions.
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Though sociologists have been interested in how temporal patterns of sociability vary in urban contexts, the study of city-level dynamics at short timescales has been challenging historically. Social media and new computational methods... more
Though sociologists have been interested in how temporal patterns of sociability vary in urban contexts, the study of city-level dynamics at short timescales has been challenging historically. Social media and new computational methods provide a solution. Our study clusters cities using sociality as a metric. We collected three months of social media data to investigate variation in the temporal structure of sociability across American cities. We find that cities cluster into three distinct types (‘Coastal’, ‘Transitional’ and ‘Heartland’) and that geographic proximity together with race, education and language associate with this clustering. Specifically, we found that clusters of Blacker cities tend to tweet more per capita, but also that more highly educated cities tend to tweet less per capita. These findings provide evidence that social media may be facilitating new opportunities to empower traditionally marginalized urban groups, a conclusion relevant to #BlackLivesMatter, the...
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Social media plays a key role in disaster rescues, and it can facilitate feelings of support when people need rescue or want to tap into neighborhood relationships. Using semi-structured interviews of people affected by Hurricane Harvey... more
Social media plays a key role in disaster rescues, and it can facilitate feelings of support when people need rescue or want to tap into neighborhood relationships. Using semi-structured interviews of people affected by Hurricane Harvey in the Greater Houston area, we addressed our research questions around notions of social support. Using photo elicitation analysis and constant comparison analysis, three overarching themes emerged from the data that inform how social support functions in this context: (1) appreciation posts are a form of emotional support; (2) resources are a form of instrumental support; and (3) helpfulness is a form of informational support. Importantly, these support functions are not isolated, and they can appear in response to an explicit request, as an anticipated need, and as an emergent reaction to a different form of social support. We also find some support for intercultural differences especially considering that our Chinese respondents preferred to use WeChat to request resources and rescues, while other non-Chinese respondents predominately used Facebook. In addition, we found that neighborhood relationships were strengthened, and social support was spread through social media.
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Social media such as Twitter and Instagram are fast, free, and multicast. These attributes make them particularly useful for crisis communication. However, the speed and volume also make them challenging to study. Historically,... more
Social media such as Twitter and Instagram are fast, free, and multicast. These attributes make them particularly useful for crisis communication. However, the speed and volume also make them challenging to study. Historically, journalists controlled what/how images represented crises. Large volumes of social media can change the politics of representing disasters. However, methodologically, it is challenging to study visual social media data. Specifically, the process is usually labour-intensive, using human coding of images to discern themes and subjects. For this reason, Studies investigating social media during crises tend to examine text. In addition, application programming interfaces (APIs) for visual social media services such as Instagram and Snapchat are restrictive or even non-existent. Our work uses images posted by Instagram users on Twitter during Hurricane Sandy as a case study. This particular case is unique as it is perhaps the first US disaster where Instagram play...
Twitter has become a household name, discussed both for its role in prominent national elections, natural disasters, and political movements, as well as for what some malign as narcissistic “chatter.” This book takes a critical step back... more
Twitter has become a household name, discussed both for its role in prominent national elections, natural disasters, and political movements, as well as for what some malign as narcissistic “chatter.” This book takes a critical step back from popular discourse and media coverage of Twitter, to present the first balanced, scholarly engagement of this popular medium.
In this timely and comprehensive introduction, Murthy not only discusses Twitter’s role in our political, economic, and social lives, but also draws a historical line between the telegraph and Twitter to reflect on changes in social communication over time. The book thoughtfully examines Twitter as an emergent global communications medium and provides a theoretical framework for students, scholars, and tweeters to reflect critically on the impact of Twitter and the contemporary media environment. The book uses case studies including citizen journalism, health, and national disasters to provide empirically rich insights and to help decipher some of the ways in which Twitter and social media more broadly may be shaping contemporary life.
In this timely and comprehensive introduction, Murthy not only discusses Twitter’s role in our political, economic, and social lives, but also draws a historical line between the telegraph and Twitter to reflect on changes in social communication over time. The book thoughtfully examines Twitter as an emergent global communications medium and provides a theoretical framework for students, scholars, and tweeters to reflect critically on the impact of Twitter and the contemporary media environment. The book uses case studies including citizen journalism, health, and national disasters to provide empirically rich insights and to help decipher some of the ways in which Twitter and social media more broadly may be shaping contemporary life.
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When natural disasters occur, various organizations and agencies turn to social media to understand who needs help and how they have been affected. The purpose of this study is twofold: first, to evaluate whether hurricane-related tweets... more
When natural disasters occur, various organizations and agencies turn to social media to understand who needs help and how they have been affected. The purpose of this study is twofold: first, to evaluate whether hurricane-related tweets have some consistency over time, and second, whether Twitter-derived content is thematically similar to other private social media data. Through a unique method of using Twitter data gathered from six different hurricanes, alongside private data collected from qualitative interviews conducted in the immediate aftermath of Hurricane Harvey, we hypothesize that there is some level of stability across hurricane-related tweet content over time that could be used for better real-time processing of social media data during natural disasters. We use latent Dirichlet allocation (LDA) to derive topics, and, using Hellinger distance as a metric, find that there is a detectable connection among hurricane topics. By uncovering some persistent thematic areas and topics in disaster-related tweets, we hope these findings can help first responders and government agencies discover urgent content in tweets more quickly and reduce the amount of human intervention needed.
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The increasing popularity of multimedia messages shared through public or private social media spills into diverse information dissemination contexts. To date, public social media has been explored as a potential alert system during... more
The increasing popularity of multimedia messages shared through public or private social media spills into diverse information dissemination contexts. To date, public social media has been explored as a potential alert system during natural disasters, but high levels of noise (i.e. non-relevant content) present challenges in both understanding social experiences of a disaster and in facilitating disaster recovery. This study builds on current research by uniquely using social media data, collected in the field through qualitative interviews, to create a supervised machine learning model. Collected data represents rescuers and rescuees during the 2017 Hurricane Harvey. Preliminary findings indicate a 99% accuracy in classifying data between signal and noise for signal-to-noise ratios (SNR) of 1:1, 1:2, 1:4, and 1:8. We also find 99% accuracy in classification between respondent types (volunteer rescuer, official rescuer, and rescuee). We furthermore compare human and machine coded attributes, finding that Google Vision API is a more reliable source of detecting attributes for the training set.
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Payment infrastructures are going through rapid change with the rise of next generation mobile networks and smartphone ownership. From mobile wallets to rideshare apps, social payments allow users to split receipts with friends, charge... more
Payment infrastructures are going through rapid change with the rise of next generation mobile networks and smartphone ownership. From mobile wallets to rideshare apps, social payments allow users to split receipts with friends, charge exes for breakup expenses, or troll celebrities. New layers of data, sociality, and markets are being created and influenced by expanding economic imaginaries, regulations, and business models leveraging these new infrastructures. In this paper we discuss how mobile payment systems have become social media. After discussing the recent history of mobile payments innovation-SMS, mobile wallets, delivery and ridesharing apps-we examine Venmo, a social payments platform that allows users to broadcast transactions to a social activity stream or public transaction feed. Our findings detail how transaction feeds of mobile payments support social practices, communication, and commerce with mobile devices and wireless networks. We present findings from a case study on Venmo to develop some implications for the design, study, and impact of mobile payment infrastructures as social media. CCS CONCEPTS • Human-centered computing~Collaborative and social computing • Human-centered computing~Social media
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When wide-scale flooding occurs in a community not accustomed to floods, health concerns emerge. While official organizations tasked with communicating emerging health information exist, the proliferation of social media makes it possible... more
When wide-scale flooding occurs in a community not accustomed to floods, health concerns emerge. While official organizations tasked with communicating emerging health information exist, the proliferation of social media makes it possible for average citizens to participate in this conversation. This study used a combination of semi-structured interviews and photo elicitation techniques to explore how citizens used private social media sites to share health information. We found two main categories of health concerns: existing medical conditions and water-created. We further identified six themes that describe the common approaches average citizens used to share health information: Narrating a personal experience, presenting it as a Public Service Announcement, downplaying the contribution, bringing a credible source into the conversation, including external links and sources, and using humor. Together, these findings suggest that citizens need health information during a flood disaster, and when they do not have it available from official sources, they use their private social media to tap into a shared community identity and carefully help one another.
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Widespread disasters can overload official agencies' capacity to provide assistance, and often citizen-led groups emerge to assist with disaster response. As social media platforms have expanded, emergent rescue groups have many ways to... more
Widespread disasters can overload official agencies' capacity to provide assistance, and often citizen-led groups emerge to assist with disaster response. As social media platforms have expanded, emergent rescue groups have many ways to harness network and mobile tools to coordinate actions and help fellow citizens. This study used semi-structured interviews and photo elicitation techniques to better understand how wide-scale rescues occurred during the 2017 Hurricane Harvey flooding in the Greater Houston, Texas USA area. We found that citizens used diverse apps and social media-related platforms during these rescues and that they played one of three roles: rescuer, dispatcher, or information compiler. The key social media coordination challenges these rescuers faced were incomplete feedback loops, unclear prioritization, and communication overload. This work-in-progress paper contributes to the field of crisis and disaster response research by sharing the nuances in how citizens use social media to respond to calls for help from flooding victims.
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This paper is the Introduction to the 2016 Proceedings of the International Conference on Social Media and Society, an annual gathering of leading social media researchers from around the world. Now, in its 7th year, the 2016 conference... more
This paper is the Introduction to the 2016 Proceedings of the International Conference on Social Media and Society, an annual gathering of leading social media researchers from around the world. Now, in its 7th year, the 2016 conference is hosted at Goldsmiths, University of London, UK from July 11 to 13. The conference’s intensive three-day program features 24 full papers,
65 work-in-progress papers, 8 workshops, 4 panels, and 34 posters. The Proceedings features 24 full papers grouped into five broad categories: Politics, Visual(izing) Social Media, Business, Places & Spaces, and Online & Offline Communities.
65 work-in-progress papers, 8 workshops, 4 panels, and 34 posters. The Proceedings features 24 full papers grouped into five broad categories: Politics, Visual(izing) Social Media, Business, Places & Spaces, and Online & Offline Communities.
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This chapter identifies a number of the most common data mining toolkits and evaluates their utility in the extraction of data from heterogeneous online social networks. It introduces not only the complexities of scraping data from the... more
This chapter identifies a number of the most common data mining toolkits and evaluates their utility in the extraction of data from heterogeneous online social networks. It introduces not only the complexities of scraping data from the diverse forms of data manifested in these sources, but also critically evaluates currently available tools. This analysis is followed by a presentation and discussion on the development of a hybrid system, which builds upon the work of the open-source Web-Harvest framework, for the collection of information from online social networks. This tool, VoyeurServer, attempts to address the weaknesses of tools identified in earlier sections, as well as prototype the implementation of key
functionalities thought to be missing from commonly available data extraction toolkits. The authors conclude the chapter with a case study and subsequent evaluation of the VoyeurServer system itself. This evaluation presents future directions, remaining challenges, and additional extensions thought to be important to the effective development of data mining tools for the study of online social networks.
functionalities thought to be missing from commonly available data extraction toolkits. The authors conclude the chapter with a case study and subsequent evaluation of the VoyeurServer system itself. This evaluation presents future directions, remaining challenges, and additional extensions thought to be important to the effective development of data mining tools for the study of online social networks.
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This chapter reviews existing data mining tools for scraping data from het-erogenous online social networks. It introduces not only the complexities of scraping data from these sources (which include diverse data forms), but also presents... more
This chapter reviews existing data mining tools for scraping data from het-erogenous online social networks. It introduces not only the complexities of scraping data from these sources (which include diverse data forms), but also presents currently available tools including their strengths and weaknesses. The chapter introduces our solution to effectively mining online social networks through the development of VoyeurServer, a tool we designed which builds upon the open-source Web-Harvest framework. We have shared details of how VoyeurServer was developed and how it works so that data mining developers can develop their own customized data mining solutions built upon the Web-Harvest framework. We conclude the chapter with future directions of our data mining project so that developers can incorporate relevant features into their data mining applications.
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Facebook is an online social networking site, which as of spring 2018 had over 1.45 billion daily active users and 2.2 billion monthly active users, built over 14 years. It is America's most popular social media site. The platform aims to... more
Facebook is an online social networking site, which as of spring 2018 had over 1.45 billion daily active users and 2.2 billion monthly active users, built over 14 years. It is America's most popular social media site. The platform aims to foster interactions between "friends" through updates, sharing content, and playing games together.
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The Facebook Psychology ‘experiment’ which manipulated the emotional content of nearly 700,000 users provides evidence that corporations need to have review procedures in terms of ethics that universities of been developing for some years... more
The Facebook Psychology ‘experiment’ which manipulated the
emotional content of nearly 700,000 users provides evidence
that corporations need to have review procedures in terms of
ethics that universities of been developing for some years surrounding social media research. In a university context, Institutional Review Boards (IRBs) are responsible for monitoring the ethics of any research conducted at the University. The US government’s Department of Health and Human Services publishes very detailed guidance for human subjects research. Section 2(a) of their IRB guidelines states that “for the IRB to approve research […] criteria include, among other things […] risks, potential benefits, informed consent, and safeguards for human subjects”. Most IRB’s take this mission quite seriously and err on the side of caution as people’s welfare is at stake.
emotional content of nearly 700,000 users provides evidence
that corporations need to have review procedures in terms of
ethics that universities of been developing for some years surrounding social media research. In a university context, Institutional Review Boards (IRBs) are responsible for monitoring the ethics of any research conducted at the University. The US government’s Department of Health and Human Services publishes very detailed guidance for human subjects research. Section 2(a) of their IRB guidelines states that “for the IRB to approve research […] criteria include, among other things […] risks, potential benefits, informed consent, and safeguards for human subjects”. Most IRB’s take this mission quite seriously and err on the side of caution as people’s welfare is at stake.
Research Interests:
As social media technologies such as Twitter, Instagram, and YouTube have become highly ubiquitous, social life itself has become reconfigured. Though early notions of an offline/online binary remain in some quarters of social research,... more
As social media technologies such as Twitter, Instagram, and YouTube have become highly ubiquitous, social life itself has become reconfigured. Though early notions of an offline/online binary remain in some quarters of social research, there is a realization amongst most that this binary is reified. As such, the study of social interactions within social media is a fundamental sociological question. This chapter argues that social researchers need to engage with the study of social media in order to comprehensively understand modern social life. This chapter also provides insights into how we, as social researchers, can critically collect and discern social formations via social media. Twitter is specifically used in this chapter to provide an example of how the medium provides opportunities for mixed qualitative and quantitative social analysis. Ultimately, this chapter also argues that the understanding of large social questions is increasingly contingent on us deciphering and understanding social knowledge formed and maintained within social media platforms.