Social Media Research Toolkit List

This toolkit compiled by the Social Media Lab seeks to provide an overview of some of the many open access tools available for the study and analysis of social media and online communities.

The table below presents the tools in alphabetical order and highlights the social media platforms they support and the features they provide. The tools in this list offer varying degrees of analysis. The list is not exhaustive and will be reviewed, updated, and enhanced on an ongoing basis.

We welcome suggestions for addition. If there are tools you use that are not on the list, please, use the comment box below to make a recommended addition.





“ is a Twitter analytics application that gives you rich insights about any public Twitter profile. This tool also gathers the requested user’s profile and latest tweets, and  analyzes the tweet’s contents to tell you about topics usage in form of ‘tag clouds’, so that you can easily understand which words were the most popular. also shows you “things not shown on public Twitter pages, such as join date,timezone and followers ratio.”

Journal articles

Waddell, D. C., Barnes, M., & Khan-Hernahan, S. (2012). Tapping into the power of Twitter: A look at Its potential in Canadian health libraries. The Canadian Journal of Library and Information Practice and Research, (7)2.

Notice from the developers: Due to changes to the Facebook API and a lack of funding to support continued development, we regret to accounce that NameGenWeb will be offline for the forseeable future.

“Program designed to help users capture, analyse, and visualize Facebook networks.” The user can select from a variety of default (gender, profile pic, locale, mutual friend count) and extended (biography, hometown, birthday, political beliefs, relationship status, religious beliefs, likes count, friend count) attributes to be used in the analysis.

Journal articles

Salter-Townshend, M. (2012). Analysing my Facebook friends. Significance, 9(4), 40-42.






“Netlytic is a cloud-based text and social networks analyzer that can automatically summarize large volumes of text and discover social networks from online conversations on social media sites such as Twitter, Youtube, blogs, online forums and chats.”  Using Netlytic, users can:

  1. capture or import online conversational type data such as tweets, blog comments, forum postings and text messages, etc
  2. find and explore emerging themes of discussions among individuals within the data set,
  3. build and visualize communication networks to discover and explore emerging social connections between individuals within online communities.

Journal articles

Caroline Haythornthwaite and Anatoliy Gruzd. “Analyzing Networked Learning Texts.” Proceedings of Networked Learning Conference (2008): 136-143;

Anatoliy Gruzd, “Studying Collaborative Learning Using Name Networks.” Journal of Education for Library and Information Science 50, no. 4 (2009): 243-253;

Anatoliy Gruzd, “Automated Discovery of Emerging Online Communities Among Blog Readers: A Case Study of a Canadian Real Estate Blog,” (conference paper, Internet Research 10.0 Conference, Milwaukee, WI, October 7-11, 2009);

Chung Joo Chung, Anatoliy Gruzd, and Han Woo Park, “Developing an e-Research Tool for Humanities and Social Sciences: Korean Interent Network Miner on Blogosphere,” Journal of Humanities 60, no.12 (2010): 429-446.

Keith N. Hampton, “Internet Use and the Concentration of Disadvantage: Glocalization and the Urban Underclass,” American Behavioral Scientist 53, no. 8 (2010): 1111-1132.

Anatoliy Gruzd, Yuri Takhteyev, and Barry Wellman. “Imagining Twitter as an Imagined Community.” American Behavioral Scientist, Special Issue on Imagined Communities 55, no. 10 (2011): 1294-1318.

Jennifer Grek Martin, “Two Roads to Middle-earth Converge: Observing Text-based and Film-based Mental Images from Online Fan Community.” (master’s thesis, Dalhousie University, 2011).



NodeXL flickrtwitteryoutubefacebookemail


“NodeXL is a free, open-source template for Microsoft® Excel® 2007 and 2010 that makes it easy to explore network graphs. With NodeXL, you can enter a network edge list in a worksheet, click a button and see your graph, all in the familiar environment of the Excel window.”

Published books

Hansen, D. L., Schneiderman, B., & Smith, M. A. (2010). Analyzing social media networks with NodeXL: Insights from a connected world. Amsterdam: M. Kaufmann.

Hansen, D. L. (2011). Exploring social media relationships. Emerald, 19(1), 43-51.

Journal articles

Gomez, J. A. G., & Shneiderman, B. (2011). Understanding social relationships from photo collection tags. Human-Computer Interaction Lab & Department of Computer Science. Retrieved from

Ahn, J.-W., Sopan, A., Shneiderman, B., Taieb-Maimon, M., & Plaisant, C. (2011). Temporal visualization of social network dynamics: Prototypes for nation of neighbors. Lecture Notes in Computer Science, 6589, 309-316.

Al-Khalifa, H. S., & 13th International Conference on Information Integration and Web-Based Applications and Services, iiWAS2011. (2011). Exploring political activities in the Saudi Twitterverse. ACM International Conference Proceeding Series, 363-366.

Black, L., Welser, H., Cosley, D., & DeGroot, J. (January 01, 2011). Self-governance through group discussion in Wikipedia: Measuring deliberation in online groups. Small Group Research, 42(5), 595-634.


TAGS & TAGS Explorertwitter

TAGS (Twitter Archiving Google Spreadsheet) is a Google Spreadsheet tool developed by Martin Hawksey that allows users to create an auto-updating archive of Tweets based on hashtags or search terms. TAGS requires a Twitter account and Twitter API authentication, and can collect up to 18,000 tweets into a single Google Spreadsheet.

TAGS Explorer is a social network visualization interface for TAGS. Explorer analyses TAGS archives by extracting and visualizing conversations between people into a network diagram using the Google Visualization API. Explorer allows users to: view the number of tweets, the number of replies, and the number of mentions that each user has in the archive. Conversations can also be replayed, and the visualization indicates whether connections were retweets, replies, or mentions.



Texifter twitter

“Texifter improves efficiency by streamlining the process of sorting large amounts of unstructured text. Texifter offers off-the-shelf enterprise class business applications specifically developed to meet the complex needs of researchers and federal rule writers. Texifter utilizes SaaS & cloud-based solutions for topic modeling, duplicate detection, and other information retrieval tasks involving users in an active learning loop”.


Textexture youtube



“Textexture visualizes any text as a network and enables the user to use this interactive visualization to read through the text in a non-linear fashion. Using the network one can see the most relevant topics inside the text organized as distinctively colored clusters of nodes, their relationship to one another, and the most influential words inside the text, responsible for topic shifts. This way the user can navigate right into the topic of the text that is the most relevant to them and use the bigger (more influential) nodes to shift into another subject.”

Journal articles

Waddell, D. C., Barnes, M., & Khan-Hernahan, S. (2012). Tapping into the power of Twitter: A look at Its potential in Canadian health libraries. The Canadian Journal of Library and Information Practice and Research, (7)2.

Other articles

Paranyushkin, D., & Nodus Labs. (2011). Identifying the pathways for meaning circulation using text network analysis. Retrieved from


Truthy twitter


“Truthy is a system to analyze and visualize the diffusion of information on Twitter. The Truthy system evaluates thousands of tweets an hour to identify new and emerging bursts of activity around memes of various flavors. The data and statistics provided by Truthy are designed to aid in the study of social epidemics: How do memes propagate through the Twittersphere? What causes a burst of popularity?” The current focus of Truthy is on: politics, social movements, and news from past 90 days.

Users can search Truthy’s collection of Memes (#hashtag, @user, http://url, or “phrase”) from the past 90 days of Twitter communication.

For each meme, Truthy calculates statistics and provides interactive interfaces that visualize the networks, allows the identification of interesting users, and makes data available for download. User-generated definitions from are shown, as are a timeline of collected tweets and the static diffusion network.

“Truthy uses a sophisticated combination of text and data mining, social network analysis, and complex networks models.”

Journal articles

McKelvey, Karissa, Rudnick, Alex, Conover, Michael D., & Menczer, Filippo. (2012). Visualizing Communication on Social Media: Making Big Data Accessible.

Menczer, F., & 2012 1st ACM Workshop on Politics, Elections and Data, PLEAD 2012 – Co-located with CIKM 2012. (2012). The diffusion of political memes in social media. International Conference on Information and Knowledge Management, Proceedings.

Weng, L., Flammini, A., Vespignani, A., & Menczer, F. (2012). Competition among memes in a world with limited attention. Scientific Reports, 2.



Tweet Archivist twitter and tagsleuth



“Tweet Archivist collects data directly from Twitter based on the user’s search terms. The user can also use this tool analyze the data to show information such as top users, words, URLs, hashtags, and more. Finally, users can download the dataset as either an Excel or PDF file.”

Tweet Archivist offers free searches, analysis, and download for single datasets, but requires a fee for ongoing collection and archiving.

Journal articles

Kavanaugh, A., Yang, S., Sheetz, S., Li, L. T., & Fox, E. (2011). Microblogging in crisis situations: Mass protests in Iran, Tunisia, Egypt. ACM: CHI.


Last updated: March 1, 2015


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