These new communication platforms afford the ability to examine social data on a variety of topics, on a massive scale, and over short periods of time. As such, they have sparked the interest of many researchers seeking to better understanding social relationships and behavior Brickman Bhutta ; Golder and Macy ; Heaivilin et al. Although some researchers have begun to use data from social media spaces such as Twitter to document changing moods and other sentiments on the aggregate level Diakopoulos and Shamma ; Golder and Macy ; Naaman, Becker, and Gravano ; Reips and Garaizar ; Yardi and Boyd , the potential of such data for demographic research has yet to be fully realized.
The ability to aggregate vast amounts of digital traces of human behavior through social media platforms represents a new data collection paradigm for social science research Cambria, Wang, and White ; Lazer et al. In some ways, data produced online parallel more well-researched methods of data collection such as surveys or structured observations, but they also contain unique properties and new features. Surveys, for example, ask respondents to recall behaviors or sentiments retrospectively with limited scope, whereas social media afford the opportunity to observe personal expression and human interaction in real time and on a large scale.
With appropriate infrastructure, social scientists can analyze social media data and begin presenting results within a matter of months or sooner , rather than the years typically required for survey-based data collection. Social media data also share some characteristics of observational or ethnographic work. Specifically, social media data allow researchers to collect reports of behaviors that are unsolicited and unprompted by the researcher.
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One might even argue that these data provide a better reflection of day-to-day social experiences than ethnographic work. Unlike previous observational work, however, activity on social media platforms can be captured and stored. This is in contrast to face-to-face interactions, which once passed cannot be reconstructed.
Using Twitter for Demographic and Social Science Research: Tools for Data Collection and Processing
A researcher conducting off-line observational work is left with only his or her perceptions and notes of what transpired. Interaction on social media, however, is preserved and can be reviewed multiple times and passed to other interested researchers. Despite these exciting advantages, social scientists often see social media data as inaccessible for social science research and solely relevant to computer scientists and others in related fields.
Golder and Macy Beyond technical skills, using social media data—in particular Twitter data—for social science research is challenging due to the fact that there are numerous differences between Twitter data and surveys collected using traditional sampling techniques. When using traditional surveys, for example, researchers have comparatively few respondents but have a great deal of control over what information respondents provide. Under these conditions, respondents provide information of interest to the researchers, but the limited sample size may not produce enough variability to study less commonly observed phenomena in their entirety e.
Data from Twitter, in many ways, are just the opposite. They are completely unsolicited but offer unprecedented volume and variability. In addition to challenges associated with specific qualities of the data, it is difficult to gather demographic data from text-based blogs and microblogs such as Twitter.
Indeed, in many online platforms, users give few or no explicit, self-reported characteristics. Demographic information is at the heart of many, if not most, social science analyses. It is often vital that researchers are able to utilize information on race, age, and gender to examine differential patterns in attitudes and behaviors.
What Are Twitter Mentions?
Removing the actual and perceived barriers that prevent social scientists from fully utilizing social media data expands research opportunities for social scientists and increases the potential for interdisciplinary research between computer scientists or statisticians and social and behavioral scientists, thus increasing the potential of studying complex social problems. Focusing on the above-mentioned challenges, this article will describe an accurate and reliable data processing infrastructure that facilitates access to demographic information encoded as images within Twitter data.
Furthermore, it seeks to encourage social scientists to consider Twitter as a valuable source of demographic information to answer relevant social science questions. To help illustrate this process, we examine a specific behavior—reporting an intention to not vote in the presidential election.
This case study demonstrates the validity of the proposed methods as a toolkit that can be modified and applied to different questions and contexts. In the following sections, we outline our approach to processing data from Twitter for demographic research and offer verification of the accuracy and reliability of these methods. We begin with an introduction to Twitter and a discussion of the resources and challenges associated with using Twitter data for social science research.
We illustrate the possible use of these methods by examining the racial composition of a small sample of Twitter users who state an intention to not vote in the presidential election. We conclude by addressing the benefits and challenges associated with this method of data collection, as well as the potential for future research that uses demographic data obtained from Twitter. It is our intention to present a flexible framework that can be used and adapted by researchers with varying experience regarding the collection and management of online data.
The platforms we feature to collect and analyze data are not the only tools available to researchers, and while we outline our processes, we seek to highlight other potential pathways for analysis. It is important to note that while social media data are often categorized as big data, these methods intend to focus on the research capacities of social media data that are not directly tied to the scale of the data.
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Although we seek to propose methods that are semiscalable and can be applied to data sets that would be time consuming for small teams of individual coders to assess, our primary goal is to leverage user metadata in a way that helps make Twitter data more accessible to the field of social science and demographic research. We believe that social media pose a number of benefits to social science research aside from their scale and we seek to highlight these capacities within this study. In addition to attitude and trend tracking, Twitter data can prove useful in the field of population health.
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Achrekar and colleagues , for example, track Twitter posts containing mentions of influenza in order to create a real-time illustration of the spread of the illness. Heaivilin and colleagues use Twitter data as a means of gathering information on the prevalence of oral health problems and the actions taken to remedy them. As these authors suggest, the candidness of Twitter users in discussing personal matters—such as oral health or emotional status—suggests that health-care providers may begin to use this platform as a tool for monitoring the public's health and communicating with patients.
Regardless of its specific application, these studies and others suggest that Twitter provides a convenient means of developing a broad understanding of a population's activities and attitudes. This is an unprecedented form of data for social scientists with broad research potential, but its use presents new methodological challenges Lazer et al. The ability to systematically gather demographic data from this source would greatly expand the potential for research that seeks to capitalize on its availability.
One popular application of Twitter data is political analysis and election forecasting. Prompted by promising analyses of political opinion trends within the blogosphere and other social media outlets, some scholars have explored whether Twitter provides a useful outlet for examining political preferences as they develop. There are a number of interesting applications of Twitter data in this burgeoning body of research.
Tumasjan and colleagues , for example, collected , Twitter messages prior to the German federal election, analyzed these tweets for mentions of party affiliation and positive or negative sentiment, and were able to effectively conclude that the opinion trends reflected in their data paralleled the results of the election.
Politicians themselves have noted the popularity of Twitter as a tool for political exchange and many now use this platform as a means of reaching out to potential constituents, though the precise relationship between electoral vulnerability and adoption is open to further exploration Lassen and Brown Some researchers, such as Conover and colleagues , have found ways to better understand the dynamics of political discussion within Twitter by mapping interaction and tweet sharing along partisan lines.
While these studies carry limitations—such as the ability to gather information only from those who volunteer their opinion or the representation of politically active individuals on Twitter—they nonetheless do highlight the potential uses of Twitter data within this analytical arena. Although many researchers have found ways to examine political trends using Twitter data, these studies unanimously lack thorough consideration of the demographic trends underlying such phenomena.
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Adding dimension would greatly benefit researchers looking to predict political outcomes or track changing tides in political opinion or participation among individuals of particular groups, because it would allow for a more nuanced view of behavior. This trend is not unique to political analysis, and the same can be said of other research that makes use of Twitter data.
The addition of demographic data to social media data sets could greatly expand the explanatory and predictive power of analyses that use these data. In the following sections, we propose a method for gathering demographic data in conjunction with information on collective voting intentions as a means of expanding upon existing applications of social media data within social science research.
The objective of this study is to establish a systematic approach to gathering demographic information about Twitter users, thus overcoming an important limitation of this rich data source. The first step we take is to choose a focal population. This analysis will focus specifically on extracting the demographic characteristics of individuals who express an intention to not vote in the presidential election.
Subsequent steps involve 1 collecting relevant user data from Twitter, 2 organizing and cleaning the resulting data, and 3 extracting demographic information about the users by utilizing profile content. Following these steps, we assess the reliability and accuracy of the demographic information obtained. The analysis presented here stands out from similar work in two important ways.
Introduction
First, it focuses not on what is said i. Doing so will allow social scientists to not only track trends and opinions using Twitter but also to examine the demographic characteristics of opinion-based networks and to better predict behaviors and attitudes based on social connections. Twitter is a microblogging platform that allows users to record their thoughts in characters or less.
The text-based content of these messages may include personal updates, humor, or thoughts on media and politics. Besides projecting their thoughts independently, users can communicate with one another through private messages, by re-tweeting one another's tweets, or by using the reply command to flag content for or about specific users. Twitter users may also contribute to broader conversations channels by including a hashtag identifier in their tweet. Twitter was originally intended to be used on mobile devices to facilitate frequent updating, but tweets can also be sent using other Internet-capable devices including tablets and personal computers.
Self-presentation Goffman on Twitter is negotiated through active conversation as well as the maintenance of personal profiles. To generate this conversation, Twitter users project their thoughts toward an imagined audience of networked individuals Marwick and Boyd , some of whom bear reciprocal ties to the users themselves and some of whom do not. This is different from other social networking sites such as Facebook, in which all users are reciprocally tied to one another and disclose information mutually.