• Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
No Downloads

Views

Total Views
685
On Slideshare
0
From Embeds
0
Number of Embeds
3

Actions

Shares
Downloads
0
Comments
0
Likes
1

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide
  • The first component includes three significant benefits related to information gathering (following other researchers' work, keeping up to date with topic, discovering new ideas or publications), and it explains 24% of the total variance. The second component includes significant benefits related to socializing and information dissemination (making new research contacts, promoting current work/research) and a negative relationship with “keeping up to date with topic”. It explains 16% of the total variance. By plotting the components in two dimensions, we discovered three clusters of related benefits, and not four as we initially anticipated (see Figure 1).

Transcript

  • 1. Research at the Social Media Lab Anatoliy Gruzd @gruzd gruzd@dal.ca Associate Professor, School of Information Management Director, Social Media Lab Faculty of Management / Faculty of Computer Science Dalhousie University Slides available at http://slideshare.net/primath
  • 2. Outline • Social Media Lab Introduction • Studying Online Social Networks • Sample Projects • Future Work Anatoliy Gruzd Twitter: @gruzd 2
  • 3. Dalhousie University (Part of U15 in Canada) Faculty of Management School of Information Management Social Media Lab
  • 4. Social Media Lab
  • 5. Welcome to the Social Media Lab Source: http://youtu.be/MDFWIZW10Ak Anatoliy Gruzd Twitter: @gruzd 6
  • 6. Netlytic.org - a cloud-based analytic tool Anatoliy Gruzd Networks Content Stats for automated text analysis & discovery of social networks from online communication Twitter: @gruzd 7
  • 7. SocialMediaAndSociety.com
  • 8. Outline • Social Media Lab Introduction • Studying Online Social Networks • Sample Projects • Future Work Anatoliy Gruzd Twitter: @gruzd 9
  • 9. Growth of Social Media and Social Networks Data Facebook Social Media have become an integral part of our daily lives! Twitter 1B users 500M users
  • 10. Studying Online Social Networks • Forum networks • Blog networks • Friends’ networks (Facebook, Twitter, Google+, etc…) • Networks of like-minded people (YouTube, Flickr, etc…) http://www.visualcomplexity.com/vc
  • 11. How to Make Sense of Social Big Data? Anatoliy Gruzd Twitter: @gruzd 12
  • 12. How to Make Sense of Social Big Data? Social Big Data -> Visualizations -> Understanding (Development, Application & Validation) Anatoliy Gruzd Twitter: @gruzd 13
  • 13. How to Make Sense of Social Big Data? Social Network Analysis (SNA) • Nodes = People • Edges /Ties (lines) = Relations/ “Who retweeted/ replied/ mentioned whom” Anatoliy Gruzd Twitter: @gruzd 14
  • 14. Advantages of Social Network Analysis • Reduce the large quantity of data into a more concise representation • Makes it much easier to understand what is going on in a group Anatoliy Gruzd Once the network is discovered, we can find out: • How do people interact with each other, • Who are the most/least active members of a group, • Who is influential in a group, • Who is susceptible to being influenced, etc… Twitter: @gruzd 15
  • 15. How Do We Collect Information About Online Social Networks? Common approach for collecting social network data: • Self-reported social network data may not be available/accurate • Surveys or interviews Problems with surveys or interviews • • • • • Time-consuming Questions can be too sensitive Answers are subjective or incomplete Participant can forget people and interactions Different people perceive events and relationships differently Anatoliy Gruzd Twitter: @gruzd 16
  • 16. How Do We Collect Information About Social Networks? • Common approach: surveys or interviews • A sample question about students’ perceived social structures Please indicate on a scale from [1] to [5], YOUR FRIENDSHIP RELATIONSHIP WITH EACH STUDENT IN THE CLASS [1] - don’t know this person [2] - just another member of class [3] - a slight friendship [4] - a friend [5] - a close friend Alice D. [1] [2] [3] [4] [5] … Richard S. [1] [2] [3] [4] [5] Source: C. Haythornthwaite, 1999 Anatoliy Gruzd Twitter: @gruzd 17
  • 17. How Do We Collect Information About Online Social Networks? Goal: Automated Networks Discovery Challenge: Figuring out what content-based features of online interactions can help to uncover nodes and ties between group members Anatoliy Gruzd Twitter: @gruzd 18
  • 18. How to Make Sense of Social Media Data? Example: Twitter Network • Nodes = People @John • Ties = “Who retweeted/ replied/mentioned whom” • Tie strength = The number of retweets, replies or mentions @Peter @Paul Anatoliy Gruzd Twitter: @gruzd 19
  • 19. 2012 Olympics in London Twitter: @dalprof Anatoliy Gruzd
  • 20. #tarsand Twitter Community Twitter: @dalprof Anatoliy Gruzd
  • 21. #1b1t Twitter Book Club Twitter: @dalprof Anatoliy Gruzd
  • 22. Outline • Social Media Lab Introduction • Studying Online Social Networks • Sample Projects (1) Politics, (2) Academia, (3) Health • Future Work Anatoliy Gruzd Twitter: @gruzd 23
  • 23. Sample Project: Politics Political Polarization on Social Media Gruzd, A. and Roy, J (2014, forthcoming). Political Polarization on Social Media: Do Birds of a Feather Flock Together on Twitter? Policy & Internet.
  • 24. Sample Project: Politics Social Media Use during the Canadian Federal Election #CndPoli Twitter Communication Network (April 6-9, 2011) Anatoliy Gruzd Twitter: @gruzd 25
  • 25. Sample Project: Politics Social Media Use during the Canadian Federal Election • There are some pockets of political polarization on Twitter Conservative – homophily - when people in social networks tend to group around similar backgrounds and interests, including shared political views. Left Liberal • Twitter has potential for supporting open cross-ideological discourse Anatoliy Gruzd Unknown & Undecided Green Bloc – 43% of the accounts in the sample did not explicitly stated their support for any party or stated support to more than one party Spam NDP Other Manual Classification of Twitter Users based on their self-declared party affiliation Twitter: @gruzd 26
  • 26. Sample Project: Academia Social Media & Scholars • Gruzd, A., & Goertzen, M. (2013). Wired Academia: Why Social Science Scholars Are Using Social Media. The 46th Hawaii International Conference on System Sciences (HICSS): 3332-3341, DOI: 10.1109/HICSS.2013.614 • Gruzd, A., Staves, K., Wilk, A. (2012). Connected Scholars: Examining the Role of Social Media in Research Practices of Faculty using the UTAUT model. Computers in Human Behavior 28 (6), 2340-2350, DOI: j.chb.2012.07.004
  • 27. Sample Project: Academia Top five social media sites based on the frequency of use Frequent Use Nonacademic soc.networks Occasional Use Presentation sharing sites Anatoliy Gruzd Blogs Online document management Video/tele conference Twitter: @gruzd Blog Media repositories Wikis Wikis Academic soc.networks 28
  • 28. Sample Project: Academia Bottom five social media sites based on the frequency of use Not popular Virtual worlds Social bookmarking Blogs (maintain) Microblogs Bibliographic management Concerns: • time consumption • privacy • persistence of digital records • absence of professional audience Anatoliy Gruzd Twitter: @gruzd 29
  • 29. Sample Project: Academia Benefits of using social media among scholarly users Keeping up to date with topics Following other researchers' work Discovering new ideas or publications Promoting current work/research Making new research contacts Collaborating with other researchers Soliciting advice from peers Maintaining professional image Publishing findings Garnering mass media attention Discovering new funding 0% 10% 20% 30% 40% 50% 60% * Based on 315 responses Anatoliy Gruzd Twitter: @gruzd 30
  • 30. Sample Project: Academia Related benefits of social media use based on the factor analysis Social & Info Dissemination Collaboration Anatoliy Gruzd Information Gathering Twitter: @gruzd explains 16% of the total variance explains 24% of the total variance 31
  • 31. Sample Project: Health Social Media & Health Gruzd, A. & Haythornthwaite, C. (2013). Enabling Community through Social Media. Journal of Medical Internet Research 15(10):e248. DOI: 10.2196/jmir.2796
  • 32. Sample Project: Health Health Care Social Media Canada Case Study: #hcsmca Twitter Community Background • #hcsmca is a vibrant community of people interested in exploring social innovation in health care. • #hcsmca hosts a tweet chat every Wednesday at 1 pm ET. The last Wednesday of the month is our monthly evening chat at 9 pm ET. Source: http://cyhealthcommunications.wordpress.com/hcsmca-2/ Anatoliy Gruzd Twitter: @gruzd 33
  • 33. Sample Project: Health Health Care Social Media Canada Case Study: #hcsmca Twitter Community Research question: 1. What accounts for the relative longevity of this particular online community? – Is it because of the founder’s leadership and her continuing involvement in this community? – Or is there a core group of members who are also actively and persistently involved in this community? 2. What is the composition of this community? Does one’s professional role/title determine a person’s centrality within this community. Anatoliy Gruzd Twitter: @gruzd 34
  • 34. Sample Project: Health Some Topics Discussed by the #hcsmca Community Nov 14, 2012 Challenge of engaging SM to inform a research agenda Nov 21, 2012 Are healthcare blogs a useful tool for education and knowledge transfer? Number of Messages Over Time Anatoliy Gruzd Twitter: @gruzd 35
  • 35. Sample Project: Health #hcsmca Communication Network on Twitter (Nov 12 - Dec 13, 2012) Roles SM health content providers Unaffiliated individual users Communicators - not specifically health related Communicators - Health related Count Healthcare professionals 50 Health institutions 31 Advocacy 30 Students 16 Educators, professors 13 Researchers Government and health policy makers 10 110 89 74 59 4 *Roles are assigned manually Node size = In-Degree Centrality Anatoliy Gruzd Twitter: @gruzd 36
  • 36. Outline • Social Media Lab Introduction • Studying Online Social Networks • Sample Projects • Future Work Anatoliy Gruzd Twitter: @gruzd 37
  • 37. Future Work: Digging Into Data Initiative MiningBiodiversity.com Anatoliy Gruzd Twitter: @gruzd 38
  • 38. New Sage Journal: Big Data & Society http://bigdatasoc.blogspot.ca/ • • Open Access & Multidisciplinary Editors – Evelyn Ruppert (Sociology, Goldsmiths, UK); – Paolo Ciuccarelli (Density Design, Milan, IT) – Anatoliy Gruzd (School of Information Management, Dalhousie University, CA) – Adrian Mackenzie (Sociology, Lancaster, UK) – Richard Rogers (Digital Methods Initiative, Amsterdam, NL) – Irina Shklovski (Digital Media & Communication Research Group, IT University of Copenhagen, DK) – Judith Simon (Institute for Technology Assessment and Systems Analysis, Karlsruhe Institute of Technology, DE) – Matt Zook (New Mappings Collaboratory, Geography, Kentucky, US). Anatoliy Gruzd Twitter: @gruzd 39
  • 39. SocialMediaLab.ca Anatoliy Gruzd Twitter: @gruzd 40
  • 40. Research at the Social Media Lab Anatoliy Gruzd @gruzd gruzd@dal.ca Associate Professor, School of Information Management Director, Social Media Lab Faculty of Management / Faculty of Computer Science Dalhousie University Slides available at http://slideshare.net/primath