automated discovery of emerging online communities among blog readers: a case study of a canadian...

Automated Discovery of Emerging Online Automated Discovery of Emerging Online Communities Among Blog Readers: Communities Among Blog Readers:

A Case Study of a Canadian Real Estate BlogA Case Study of a Canadian Real Estate Blog

Anatoliy Gruzd [email protected]

October 11, 2009

“Computer networks are inherently social networks, linking people, organizations, and knowledge”

Wellman (2001)

Anatoliy Gruzd ([email protected])

Automated Discovery of Online Communities

2

Outline

• Why Do We Want To Discover Online Social Networks?

• How Do We Collect Information About Social Networks?

• A Case Study of a Canadian Real Estate Blog

• Future Research



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• Users

– More useful recommendation systems• Amazon, Netflix

• Social information filtering in location-based systems (Espinoza et al, 2001)

– Improve users’ experience with information systems• Keeping in touch with friends and colleagues (e.g., LinkedIn, Facebook)

• New browsing capabilities for news stories (Pouliquen et al, 2007; Tanev, 2007)

– A more secured/easy way to share private content with trusted individuals

• “Web of Trust” (Golbeck, 2008; Matsuo et.al., 2004)

Why Do We Want To Discover Online Social Networks?



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• Companies

– Recruiting talents

• Different ties for different needs (Leung, 2003)

– Finding experts • Expertise oriented searching using social networks (Ehrlich et al, 2007; Li

et al, 2007)

– Marketing • Viral marketing (Domingos, 2005)• Building brand loyalty using customer networks (Thompson & Sinha,

2008)




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• Researchers

– Ability to ask and answer deeper questions about the nature and operation of online communities

• How and why one online community emerges and another dies?

• How people agree on common practices and rules in an online community?

• How knowledge and information is shared among group members?




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Case Study: Online Communities Among Blog Readers

• Can a blog support the development of an online community?

• How do we know if a community has emerged among blog readers?



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What content-based features of online interactions help to uncover nodes and ties between online participants?

Automated Discovery of Social Networks

among Blog Readers/Commentators

?



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Outline




• Future Research



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Automated Discovery of Social Networks Name Network Approach

Method Connect the sender to people mentioned in the message

Connect people whose names co-occur in the same message(s)

Discovered Tie(s)

Ann -> Steve

Ann -> Natasha

Steve <-> Natasha

FROM: Ann

“Steve and Natasha, I couldn't wait to see your site.

I knew it was going to [be] awesome!”

This approach looks for personal names in the content of the comments to identify social connections between online participants.



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• Main Communicative Functions of Personal Names (Leech, 1999)

– getting attention and identifying addressee

– maintaining and reinforcing social relationships

• Names are “one of the few textual carriers of identity” in discussions on the web (Doherty, 2004)

• Their use is crucial for the creation and maintenance of a sense of community (Ubon, 2005)

Automated Discovery of Social Networks

Name Network Approach



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ICTA - Online Tool for Social Network Discovery http://TextAnalytics.net



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Outline




• Future Research



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Case Study: Online Communities Among Blog Readers

• Can a blog support the development of an online community?

• How do we know if a community has emerged among blog readers?



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Characteristics of Online Community

• Virtual Settlement (Jones, 1997)– virtual common-public-place

– interactivity

– sustained membership

• Sense of Community (McMillan & Chavis, 1986)– feelings of membership & influence

– reinforcement of needs

– shared emotional connection



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Comments Posted by Blog Readers



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Changes in Social Networks over Time

January 2009

October 2008

# msg 1526

# posters 198 (50)

# ties 383

# msg 3217

# posters 434 (88)

# ties 999



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SNA Statistics

October 2008 January 2009

# Active Posters 50 88

Degree Centrality 7 18

Betweenness Centrality 3 31

– the posters became more connected and more of them took a stand in a group



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Semi-automated Content Analysis with ICTA



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Results: Characteristics of Online Community

• Sustained membership– 44% (Oct 2008 -> January 2009)

• Highly interactive discussions– 1 in 3 comments directly addresses or references a fellow poster

• Interactions that are important in developing stronger connections between people

– information sharing, help, humor, and support

– “Sorry to hear about your particular financial situation”

• Self-moderation– “whatever you call the matter between your two. Please refrain from bad language”

– “ try to hear what people are saying, and not twist their words”

• Mutual awareness– “watch out for our resident <Nickname>”

– “what happened to <Nickname>?”



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Conclusions

• Social networks discovered automatically by the “name network” method are good and accurate approximations of the actual social networks among online blog readers and commentators

• Even in a blog dominated by mostly anonymous and argumentative commentators, a community can still be formed and strengthened

• Community norms provide blog readers with a “safe” environment to debate different opinions with fellow blog readers.



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Outline




• ICTA - Online Tool for Social Network Discovery

• Future Research



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Future Research

• Collaborators: Caroline Haythornthwaite (University of Illinois at Urbana-Champaign, USA) and Mark Chignell (University of Toronto, Canada)

• This project addresses the question of “What to do with a million blogs?” – Why and how people maintain existing and/or form new social relationships in

the blogosphere? – What recommendations can be made to bloggers who wish to establish a

sustainable online community around their blogs and to designers and software engineers for developing more user-friendly infrastructures to support these communities?

– How the vast amount of data generated by these online communities in a form of comments can be used to help Internet users to find and join active communities of their interests on a particular subject in the blogosphere?

(under review)

Digging Into The Blogosphere: Automated Discovery, Visualization and Evaluation of Social Networks Among Bloggers and Blog Readers



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Related Research

• Networked Learning– Gruzd, A. (2009). Studying Collaborative

Learning Using Name Networks. Journal for Education in Library and Information Science 50(4): 243-253.

– Haythornthwaite, C. and Gruzd, A. (2008). Analyzing Networked Learning Texts. In the Proceedings of Networked Learning Conference, Halkidiki, Greece.



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Related Research (cont.)

• How is Twitter Changing the Ways Scholars Disseminate Knowledge and Information?

– Gruzd, A., Takhteyev, Y. and Wellman, B. (2009). A Tweetise on Twitter: Networked Individualism Online. Thematic Session on Imagined Communities in the 21st Century, American Sociological Association, August 8-11, 2009, San Francisco, CA, USA.

– Wellman, B., Gruzd, A. and Takhteyev, Y. (2009). Networking on Twitter: A Case Study of a Networked Social Operating System. Workshop on Information in Networks (WIN) September 25-26, 2009, New York City, NY, USA.



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Related Research (cont.)

• Korean Internet Network Miner (KINM) – Analysis of Korean blogging communities on the

world-first citizen journalism site OhMyNews (http://www.ohmynews.com).

• Collaborators – Han-Woo Park (Yeungnam University, S.Korea)



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References / Related Work

• Ali-Hasan, N. and Adamic, L. (2007). Expressing Social Relationships on the Blog through Links and Comments. International Conference on Weblogs and Social Media, March 26-28, 2007, Boulder, Colorado, USA.

• Chin, A. and M. Chignell (2007). "Identifying Communities in Blogs: Roles for Social Network Analysis and Survey Instruments." International Journal of Web Based Communities 3(3): 343-365

• Domingos, P. (2005). "Mining social networks for viral marketing." IEEE Intelligent Systems 20(1): 80-82.• Ehrlich, K., C.-Y. Lin, et al. (2007). Searching for experts in the enterprise: combining text and social network

analysis. Proceedings of the 2007 international ACM conference on Supporting group work. Sanibel Island, Florida, USA, ACM.

• Espinoza, F., P. Persson, et al. (2001). GeoNotes : Social and Navigational Aspects of Location-Based Information Systems. Ubicomp 2001: Ubiquitous Computing: 2-17.

• Fisher, D., D. Fisher, et al. (2006). You Are Who You Talk To: Detecting Roles in Usenet Newsgroups. System Sciences, 2006. HICSS '06. Proceedings of the 39th Annual Hawaii International Conference on.

• Furukawa, T., Matsuo, Y., Ohmukai, I., Uchiyama, K. and Ishizuka, M. (2007). Social Networks and Reading Behavior in the Blogosphere. ICWSM 2007, Boulder, Colorado, USA.

• Golbeck, J. (2008). "Trust and Nuanced Profile Similarity in Online Social Networks." ACM Transactions on the Web.

• Golbeck, J. (2008). "Trust and Nuanced Profile Similarity in Online Social Networks." ACM Transactions on the Web.

• Haythornthwaite, C. (2006). "Facilitating collaboration in online learning." Journal of Asynchronous Learning Networks 10(1): 7-24.

• Jones, Q. (1997). Virtual Communities, Virtual Settlements And Cyber-Archaeology. Journal of Computer Mediated Communication 3(3).

• Leech, G. (1999). The Distribution and Function of Vocatives in American and British English Conversation. In H. Hasselggård and S. Oksefjell (Eds.) Out of Corpora: Studies in Honour of Stig Johansson. Amsterdam/Atlanta, GA: Rodopi.

• Leggatt, H. (2007, April 12). Spam Volume to Exceed Legitimate Emails in 2007. BizReport : Email Marketing. Retrieved October 30, 2008, from http://www.bizreport.com/2007/04/spam_volume_to_exceed_legitimate_emails_in_2007.html



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References / Related Work (cont.) • Leung, A. (2003). "Different ties for different needs: Recruitment practices of entrepreneurial firms at different

developmental phases." Human Resource Management 42(4).

• Li, J., J. Tang, et al. (2007). EOS: expertise oriented search using social networks. Proceedings of the 16th international conference on World Wide Web. Banff, Alberta, Canada, ACM Press.

• Matsuo, Y., H. Tomobe, et al. (2004). Finding Social Network for Trust Calculation. the 16th European Conference on Artificial Intelligence (ECAI2004).

• Matsuo, Y., H. Tomobe, et al. (2004). Finding Social Network for Trust Calculation. the 16th European Conference on Artificial Intelligence (ECAI2004) 16: 510.

• McMillan, D.W. & Chavis, D.M. (1986). Sense of community: A definition and theory. Journal of Community Psychology 14(1): 6-23.

• Pouliquen, B., R. Steinberger, et al. (2007). Multilingual multi-document continuously-updated social networks. Proceedings of the Workshop Multi-source Multilingual Information Extraction and Summarization (MMIES'2007) held at RANLP'2007. Borovets, Bulgaria.

• Savignon, S.J. and Roithmeier, W. (2004). Computer-Mediated Communication: Texts and Strategies. Computer Assisted Language Instruction Consortium Journal 21(2): 265-290.

• Swearingen, J. (2008). Four Ways Social Networking Can Build Business. Bnet.com. Retrieved from http://www.bnet.com/2403-13070_23-219914.html

• Tanev, H. (2007). Unsupervised Learning of Social Networks from a Multiple-Source News Corpus. Workshop Multi-source Multilingual Information Extraction and Summarization (MMIES'2007) held at RANLP'2007. Borovets, Bulgaria.

• Thompson, S. A. and R. K. Sinha (2008). "Brand Communities and New Product Adoption:The Influence and Limits of Oppositional Loyalty." Journal of Marketing 72(6): 65-80.

Automated Discovery of Emerging Online Automated Discovery of Emerging Online Communities Among Blog Readers: Communities Among Blog Readers:

A Case Study of a Canadian Real Estate BlogA Case Study of a Canadian Real Estate Blog

Anatoliy Gruzd [email protected]

October 11, 2009

“Computer networks are inherently social networks, linking people, organizations, and knowledge”

Wellman (2001)

automated discovery of emerging online communities among blog readers: a case study of a canadian...

Technology

online blog readers

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online participants

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