As a part of getting new users to test our Sifter beta, every month this summer we are awarding 12 #datagrants to academics. These prizes shave thousands of dollars of costs off of your research. The August social data and tools prize winners were: Kelli S. Burns, Ph.D. University of South Florida School of Mass Communications “I will look at the #icebucketchallenge during a particularly active time in the campaign (mid-August 2014) when several celebrities were creating a lot of attention for their videos. I plan to explore the celebrity impact on tweets as well as specific mentions of ALS in tweets about the campaign. I am also interested in conversation themes related to the campaign and how other organizations hijacked the hashtag for their own gain.” @KelliSBurns Kathleen PJ Brennan PhD Candidate at the University of Hawai’i at Manoa Political Science “I hope to use my data and software prize to study the influence of internet memes on political interest and awareness. This particular analysis will form part of a dissertation chapter on internet memes, which examines such memes as emergent agents in the overlaps of online and offline spaces. This will be my first opportunity to incorporate such data into my dissertation, and I can’t wait to get started!” @katiepbrennan Aminu Bello Phd Research Student Marketing “To analyse data from social media To find out the role of social media in CRM Data will be collected primarily from facebook and twitter pages” Ann Pegoraro Laurentian University School of Sports Administration and I am the Director of the Institute for Sport Marketing, a research center at the university “I plan on using the Texifter Data and software to further my research work in social media use in sport. In particular, the historical data will be used by my colleagues and I to investigate how the use of Twitter by athletes, teams/organizations and fans has evolved over time.” @SportMgmtProf Susan Currie Sivek Linfield College Mass Communication “I will use the prize to continue to study the relationship between journalism and social media. I am especially interested in how magazines use these media to connect to their audiences.” @profsivek Dimitrinka Atanasova Research Associate (CascEff) and PhD student Media and Communication, University of Leicester “I plan to study information sharing about obesity, specifically I hope to identify the sources behind the web links that are shared most. For my recently submitted PhD I analysed obesity-related news articles from selected online newspapers, and while it can be expected that content from these should be among the most shared, I would like to see what other information sources are read/shared.” @dbatanasova Hassan Zamir University of South Carolina School of Library and Information Science “The Texifter data prize will be primarily used as the data for writing my dissertation which focuses on how and what citizens and expatriates of Bangladesh reported about the Shahbag Movement during 2013 in Twitter. A content analysis of these tweets will be helpful to get an insight about the protest, it’s primary issues, protesters, and their concerns. The data will be useful for understanding how social media tools like Twitter increases democracy, civic engagement, and social empowerment. A potential outcome of this research will be designing a computer supported tool for better understanding worldwide social movements and mitigate the social crisis issues quickly.” @hassan_zamir Jacob Groshek Boston university Emerging media “I plan to look at how people use social media in a smoking cessation program. Or follow other emergent social situations, like Ferguson or Gaza.” @jgroshek Yunkang Yang University of Washington Department of Communication “I would use it to extract historical posts to study online discourse regarding a major public event in China in 2012, as well as the access to discover text to cleanse, code and visualize the data. I hope to group those posts into categories to show the levels of contention in discourse and to reflect the role social media play in facilitating public debate.” @yangyunkang Will Frankenstein Carnegie Mellon University Dept. Engineering & Public Policy / Center for Computational Analysis of Social and Organizational Systems “I will be using the data to explore how individuals communicate and discuss technological risk as expressed on social media. I will be focusing on discussions of nuclear proliferation. The prize is especially helpful for gauging and distinguishing the immediate social media response vs. the long-term response of major events related to nuclear materials, such as Fukushima and New START.” Micah Altman MIT Libraries: Program on Information Science “We will experiment with PowerTrack to pilot to integrate dynamic corrections to official statistics. We will experiment with DiscoverText to perform collaborative evaluation of transparency in government data and websites.” @drmaltman
As a part of getting new users to test our sifter beta, every month this summer we are awarding 12 #datagrants to academics. All you need to do to be included in the July drawing is submit a valid historical Twitter estimate request using sifter and then send us your CV. These prizes shave thousands of dollars of costs off of your research. The June social data and tools prize winners were: Kelly Fincham The Department of Journalism, Media Studies, and Public Relations at Hofstra University
“I will use the data and software prize to further my research and analysis of journalism practice on Twitter. My research agenda explores journalists’ evolving norms and practices on social media, specifically Twitter, in the U.S. and Ireland. This grant will help me to research and analyze this subject area in more depth.” @kellyfincham
“I am hoping to use the data and software prize for my PhD research on the recovery and rebuild after the Christchurch earthquake of 2011. I am particularly interested in framing and sentiment of tweets and am hoping to compare a historical data set during disaster response and recovery to the conversation about the rebuilt of the city which is still ongoing today. I am hoping to study the differences and similarities of conversations on Twitter now and then.” @tinserella
“I will like to integrate the collected data (tweets) in my final essay in order to get my Masters degree. The subject of my essay is: racism online.” @CarminaGodoy
“This award will be used to collect and analyze select data from the early group stages of the 2014 World Cup. Social media – including but not limited to Twitter – are increasingly integrated into traditional (TV, radio, print) media campaigns. At the 2014 World Cup, the hashtags #becausefootball and #becausefutbol were promoted throughout the televising of the games. Exploratory thematic analysis of these Tweets – enabled by Sifter and Discovertext – will describe how the use of these commercially-oriented hashtags are used in comparison to what we know about live event Twitter usage in the current body of research.” @warrensallen
“I plan to use the prize to capture and analyze online discussion and commentary about police use of automated license plate recognition (ALPR) systems and wearable cameras. In particular, I hope to examine discussions related to the public disclosure of data generated by these systems under freedom of information laws.” @newmedialaw
“This project will survey the current use of online social media by health organization for health campaign and analyze the reach and diffusion of campaign messages. Despite the ever growing number of online social media-based health campaigns, little work has been done to understand how interactive natures of online social media are used for public health promotion. For this project, Twitter data will be analyzed to enhance our understanding of how health organizations use social media for public health promotion and how such uses of online media platforms are received by the public.”
Abhay Gupta Lecturer at Fairleigh Dickinson University
“I plan to use it to understand the dynamics of public opinion. In particular, I want to test various hypotheses on how major events (e.g. election wins, market crash, sports results) impact the sentiment and whether pre-event opinion analysis has any predictive power in explaining actual outcomes.” @EmpForesights
“I am looking forward to using the Texifter data and software to investigate how consumers and brands communicate on social media. In particular, I’m interested in how language use affects consumer behavior in online contexts. Given the extent to which consumers have and are continuing to adopt social media, this research should have important implications for marketing practitioners.” @vabarger
“I am studying the influence of social movements on changes in the law — specifically land law. I hope to use the prize to access Twitter data that can tell me about the relationships between movement actors, how they form their interests, and how these change over time.” @jrgbaxter
“I will use the software and data to continue my study of the lifecycle of policy initiatives. I used DiscoverText in my latest book Interpreting Hashtag Politics (Palgrave Macmillan, 2014). Historic Twitter data reveals the first mention of policies that enjoy several months of widespread attention before disappearing without trace. To understand why and how this occurs, I will continue use DiscoverText to de-duplicate the dat
a and develop thematic code sets with a team of research assistants.” @SRJeffares
Cristian Vaccari Lecturer in Politics at the Royal Holloway University of London
“I am planning on using the data and software to analyze how politically motivated users of social media engage with mediated political events, such as televised leader debates and high-profile interviews, to better understand the interplay between television and social media in the flow of political messages.” 25lettori
Bill D. Herman Remember: All you need to do to be included in the July drawing is submit a valid historical Twitter estimate request using sifter and then send us your CV.
Our Vanderbilt University team uses DiscoverText (DT) to support qualitative text analysis of 8,531 high school students’ responses about their in-school experiences of bullying. DiscoverText has offered us powerful ways to perform key steps throughout our coding process. Fundamentally, DT supports parsing our large data set into archives, buckets, and datasets. Thus, we are able to focus on key portions of our large data set to hone our initial hierarchical coding structure while retaining the ability to return to an untouched dataset for final coding. We use the diverse annotation tools in DiscoverText to mark singular problematic items for discussion at meetings. Our team was able to develop a complex coding structure with 58 codes (at one point we had 128), and begin coding in a month and a half. Undoubtedly, DiscoverText’s robust organizational and annotation tools, within an easy-to-use user interface, supported expediency. Following the development of our coding structure we employed DiscoverText’s analytic tools to better understand and improve our team’s inter-coder reliability. DT’s real-time coding analytics supports decision-making in meetings. Through the use of these tools, we raised our coding reliability from a .2 Kappa value to a .82 Kappa value after five training rounds. Given that four coders are using 58 hierarchical codes to code over 8,000 free-response items, the numbers represent a phenomenal increase in reliability. Presently, we are half way through coding the 8,531 items using overlapping coding patterns to ensure reliability. Out team members share their experiences below: “I am currently working with a research team that must code students’ responses about their bullying experiences. I had never coded before and was introduced to DiscoverText only a few months ago. Fortunately, I have found DiscoverText to be very user-friendly and easy to navigate. Despite my lack of formal coding experience, I have found the program to run smoothly and have already learned a great deal in such a short period of time. My favorite feature thus far would have to be the code-by-code comparisons. This allows us to discuss any discrepancies among the research team and to increase our reliability. I have enjoyed exploring the features of this program and look forward to discovering what more it can do.” – Abbie, undergraduate, Human and Organizational Development, honors track. “My team is using DiscoverText to code thousands of brief responses to a survey question about bullying. As someone who is new to qualitative research and coding programs, I have found DiscoverText easy to use. The coding process was very easy for me to learn, and I quickly became efficient at coding responses. Our initial looks at code comparisons have been fairly straightforward for me to figure out as well. As we move forward with more analysis, I anticipate other functions and features of DiscoverText will be similarly straightforward, and I will see more of the power of the program.” – Brian, master’s student, Human Development Counseling. “I’m working with DiscoverText as part of an academic research team analyzing high school students’ qualitative responses to questions about bullying. As we have been coding responses, we have found the coding process fairly smooth, although not without a few features that we would have done differently. Still, the process of coding is similar to that of other qualitative coding software (I’ve used NVivo). We haven’t yet gotten into any sophisticated filtering or analysis, but I’m expecting that it will be really useful. The biggest impression I’m left with after my three months of using DiscoverText is that it’s a powerful tool, and we’ve only scratched the surface of what it can do.” – Ben, doctoral student, Community Research and Action. Overall, DiscoverText enabled our team’s timely progress through a complex research process. Following coding, we intend to make use of DT’s meta data “tagging” capabilities such that we can meaningfully export coded response summaries to their “tagged” respective schools. Finally, we intend to continue to explore the useful capabilities of DT in our research. We find DiscoverText easy-to-use and helpful – our questions have been kindly answered by the Textifier support team or solved through processing the helpful support material on DT’s support site! Thanks a lot DiscoverText! Joseph H. Gardella
Document relevance is a key challenge for social media research. The specific problem of “word sense disambiguation” is widespread. If I am interested in “banks” where money is stored, I want to exclude mentions of river banks. If I am “Delta” airlines, I do not want to see social data about Delta faucets, Delta force, or those pesky river deltas. If I run a sports team like the Pittsburgh Penguins, the massive numbers of Facebook posts and Tweets about flightless but adorable birds are equally problematic. There are very few social media analytics projects that can easily avoid the challenge of sorting relevant and irrelevant documents. At Texifter, we have refined a powerful set of tools and techniques for doing word sense disambiguation. This 5-minute video uses the example of Governor Chris Christie to illustrate how the five pillars of text analytics can help anyone to identify and remove irrelevant documents from an ambiguous social data collection. The principles are very similar to spam filtering in email; we use the same mathematics. Using DiscoverText, we argue an individual or small collaborative team can create a custom machine classifier for the task in just a few hours. Someday, we hope to get this down to a few minutes.
We interviewed researchers at the University of Illinois Chicago in the Health Media Collaboratory about their use of DiscoverText and the Gnip-enabled Power Track for Twitter to study smoking behavior. The team, led by Dr. Sherry Emery, explains why it is important to train and use custom machine classifiers to sort the millions of tweets they are collecting from the full Twitter fire hose. The UIC team strongly argues for the combination of good tools and highly reliable data.