Alexander Gross

Understanding Cancer-Based Networks in Twitter Using Social Network Analysis

Web-based social media networks have an increasing frequency of health-related information, resources, and networks (both support and professional). Although we are aware of the presence of these health networks, we do not yet know their ability to (1) influence the flow of health-related behaviors, attitudes, and information and (2) what resources have the most influence in shaping particular health outcomes. Lastly, the health research community lacks easy-to-use data gathering tools to conduct applied research using data from social media websites. In this position paper we discuss and sketch our current work on addressing fundamental questions about information flow in cancer-related social media networks by visualizing and understanding authority, trust, and cohesion. We discuss the development of methods to visualize these networks and information flow on them using real-time data from the social media website Twitter and how these networks influence health outcomes by examining responses to specific health messages.

Urban Social Media Demographics: An Exploration of Twitter Use in Major American Cities

This article explores intersections between place, race/ethnicity, and gender amongst American Twitter users and makes an argument that studying the intensity of tweets provides insights into how and why particular groups tweet. Given recent events in American political life such as the shooting in Ferguson, Missouri and the reactions by young, urban African Americans on Twitter, understanding the role of race, place, gender, and age is important. We observed the time between tweets of urban American Twitter users and explored whether the medium may be providing traditionally marginalized groups, such as young Black men, with potential avenues for mobilizing communication and access to resources.

Social media processes in disasters: Implications of emergent technology use

This article seeks to extend social science scholarship on social media technology use during disruptive events. Though social medias 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 Sandys 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.

Evaluation and Development of Data Mining Tools for Social Network Analysis

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.

Visual Social Media and Big Data. Interpreting Instagram Images Posted on Twitter

Abstract 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 played a key role in how victims experienced Sandy. It is also the last major US disaster to take place before Instagram images were removed from Twitter feeds. Our sample consists of 11,964 Instagram images embedded into tweets during a twoweek timeline surrounding Hurricane Sandy. We found that the production and consumption of selfies, food/drink, pets, and humorous macro images highlight possible changes in the politics of representing disasters - a potential turn from top-down understandings of disasters to bottom-up, citizen informed views. Ultimately, we argue that image data produced during crises has potential value in helping us understand the social experience of disasters, but studying these types of data presents theoretical and methodological challenges.

Pace of Life in Cities and the Emergence of Town Tweeters

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 have sublinear growth in the number of active Twitter users. This suggests that there is a more concentrated core of more active users that may serve an information broadcast function for larger cities, an emerging group of “town tweeters” as it were.