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Automated Discovery of Emerging Online Communities Among Blog Readers:  A Case Study of a Canadian Real Estate Blog Anatol...
Outline <ul><li>Why Do We Want To Discover Online Social Networks? </li></ul><ul><li>How Do We Collect Information About S...
<ul><li>Users </li></ul><ul><ul><li>More useful recommendation systems </li></ul></ul><ul><ul><ul><li>Amazon, Netflix  </l...
<ul><li>Companies  </li></ul><ul><ul><li>Recruiting talents  </li></ul></ul><ul><ul><ul><li>Different ties for different n...
<ul><li>Researchers </li></ul><ul><ul><li>Ability to ask and answer deeper questions about the nature and operation of onl...
Case Study:  Online Communities Among Blog Readers <ul><li>Can a blog support the development of an online community?  </l...
What content-based features of online interactions help to  uncover nodes and ties between online participants? Automated ...
Outline <ul><li>Why Do We Want To Discover Online Social Networks? </li></ul><ul><li>How Do We Collect Information About S...
Automated Discovery of Social Networks    Name Network   Approach FROM: Ann “ Steve  and  Natasha , I couldn't wait to see...
<ul><li>Main Communicative Functions of Personal Names (Leech, 1999)   </li></ul><ul><ul><li>getting attention and identif...
ICTA - Online Tool for Social Network Discovery  http://TextAnalytics.net
Outline <ul><li>Why Do We Want To Discover Online Social Networks? </li></ul><ul><li>How Do We Collect Information About S...
Case Study:  Online Communities Among Blog Readers <ul><li>Can a blog support the development of an online community?  </l...
Characteristics of Online Community <ul><li>Virtual Settlement (Jones, 1997) </li></ul><ul><ul><li>virtual common-public-p...
Comments Posted by Blog Readers
Changes in Social Networks over Time January 2009 October 2008 383 # ties 198 (50) # posters 1526 # msg 999   # ties 434 (...
SNA Statistics <ul><li>the posters became more connected and more of them took a stand in a group </li></ul>Betweenness Ce...
Semi-automated Content Analysis with ICTA
Results: Characteristics of Online Community <ul><li>Sustained membership </li></ul><ul><ul><li>44% (Oct 2008 -> January 2...
Conclusions <ul><li>Social networks discovered automatically by the “name network” method are good and accurate approximat...
Outline <ul><li>Why Do We Want To Discover Online Social Networks? </li></ul><ul><li>How Do We Collect Information About S...
Future Research <ul><li>Collaborators:  Caroline Haythornthwaite (University of Illinois at Urbana-Champaign, USA) and Mar...
Related Research <ul><li>Networked Learning </li></ul><ul><ul><li>Gruzd, A. (2009). Studying Collaborative Learning Using ...
Related Research (cont.) <ul><li>How is Twitter Changing the Ways Scholars Disseminate Knowledge and Information? </li></u...
Related Research (cont.) <ul><li>Korean Internet Network Miner (KINM)  </li></ul><ul><ul><li>Analysis of Korean blogging c...
References / Related Work   <ul><li>Ali-Hasan, N. and Adamic, L. (2007). Expressing Social Relationships on the Blog throu...
References / Related Work  (cont.)  <ul><li>Leung, A. (2003). &quot;Different ties for different needs: Recruitment practi...
Automated Discovery of Emerging Online Communities Among Blog Readers:  A Case Study of a Canadian Real Estate Blog Anatol...
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Automated Discovery of Emerging Online Communities Among Blog Readers: A Case Study of a Canadian Real Estate Blog

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  • How to discover social networks automatically among blog readers/commentators on a single blog ? Until now, most of the research that tried to answer this question has been conducted using very limited case studies that relied heavily on manual content analysis (e.g., Herring et al., 2005) or surveys (e.g., Blanchard, 2004). However, manual content analysis and surveys are very expensive and time consuming. They are perfectly adequate for analyzing blogs with a small number of readers who leave a relatively small number of comments, but they are not practical for the analysis of blogs that attract hundreds or even thousands of daily readers and commentators. Furthermore, this task becomes even more daunting if we want to find out exactly what is going on in more than just one blog.
  • In sum, by focusing on personal names, the ‘name network’ method can quickly identify addressees of each message and thus automatically discover “who talks to whom” in many-to-many types of online communication such as threaded discussions and chats. Furthermore, the social bond function of personal names suggests that the discovered ties between people will not just reflect communication patterns, but will also likely reflect real social relationships between people.
  • The Name Network method has been incorporated into ICTA ( http://TextAnalytics.net)
  • I used ICTA to analyze a Canadian real estate blog called “Greater Fool”. The blog was started in March 2008 by Garth Turner who is an author, financial expert and former Canadian MP. The blog covers a wide variety of topics involving both the US and Canadian real estate markets and the ongoing financial meltdown in the world economy. The primary reason this blog was chosen, because it generates long and interesting discussions. The owner of the blog posts a new entry almost every day, and each new entry in turn generates on average about 133 readers’ comments. ============== However, not all blogs are capable of fostering and sustaining a community of loyal readers. Why is that?
  • The comments are posted anonymously under user-created pseudonyms
  • The dataset for this study consisted of two separate samples: all comments posted by blog readers in October 2008 (1526 comments) and in January 2009 (3217 comments). After the data was collected, the “name network” method was used to build one social network for each of the two collection periods.
  • The community has grown from 50 active posters in October, 2008 to 88 in January, 2009. The growth and increasing popularity of the blog can also be confirmed by looking at the changes in a number of standard measures that are commonly used in social network analysis. For example, the increase in total degree centrality (from 6.9 to 18.36) and betweenness centrality (from 3.06 to 31.1) suggests that the posters became more connected and more of them took a stand in a group. The next step was to validate the social networks built automatically and determine if these networks can be used to identify and study online communities among blog readers.
  • To validate the social networks that were built automatically and determine if these networks are indeed accurate representations of online communities among blog readers, I used ICTA’s interactive network visualization features to visualize and explore connections among posters and to find out how and why they are connected. By clicking on an edge that connects a pair of the posters in the social network, I was able to read all comments that were used to establish various connections between the posters. This in turn allowed me to verify and explain the nature of the connections between posters and to characterize the discovered online community overall. By using ICTA to manually explore the data, I was able to confirm that the social networks automatically discovered by the system share many of the characteristics that would normally be classified as an actual online community.
  • I found that Blog readers voluntarily came back to the blog repeatedly over a long period of time. For example, almost half of people who commented in October also posted comments in January. … the creation of shared norms as shown through examples of the self-moderation on the blog and the presence of highly interactive discussions where 1 in 3 comments directly addresses or references a previous poster. Also by exploring the content and types of comments, it was revealed that over time the anonymous posters/readers of the blog learned to recognize and acknowledge each other and could even predict how a specific poster might respond to a particular issue. There were many instances of interactions that are important in developing stronger connections between people such as comments that provide help, support, express humor, and share information that other reader of the blog might find useful.
  • Interestingly many of the key actors in the two social networks discovered by ICTA were actually those who posted the most argumentative or controversial comments. But despite the high level of disagreement among some posters (or maybe even because of it), during the period between October and January the community has thrived and grew from a mere 50 active posters to 88. This observation is in line with the previous research on the so-called “flaming phenomena”. While studying responses to antagonism on YouTube, Lange et al. (2004) found that “not all participants view certain critical comments as a problem that necessitates regulatory mechanisms that threaten to limit participation” and “[p]articipants often wish to preserve an aura of free speech and promote self-expression” (pp.2-3). So perhaps, aside from self-interest in the primary topic of the blog, readers are coming back to this blog because it contains elements of self-moderation that many of them value.
  • Transcript of "Automated Discovery of Emerging Online Communities Among Blog Readers: A Case Study of a Canadian Real Estate Blog"

    1. 1. Automated Discovery of Emerging Online Communities Among Blog Readers: A Case Study of a Canadian Real Estate Blog Anatoliy Gruzd [email_address] October 11, 2009 “ C omputer networks are inherently social networks, linking people, organizations, and knowledge” Wellman (2001)
    2. 2. Outline <ul><li>Why Do We Want To Discover Online Social Networks? </li></ul><ul><li>How Do We Collect Information About Social Networks? </li></ul><ul><li>A Case Study of a Canadian Real Estate Blog </li></ul><ul><li>Future Research </li></ul>
    3. 3. <ul><li>Users </li></ul><ul><ul><li>More useful recommendation systems </li></ul></ul><ul><ul><ul><li>Amazon, Netflix </li></ul></ul></ul><ul><ul><ul><li>Social information filtering in location-based systems (Espinoza et al, 2001) </li></ul></ul></ul><ul><ul><li>Improve users’ experience with information systems </li></ul></ul><ul><ul><ul><li>Keeping in touch with friends and colleagues (e.g., LinkedIn, Facebook) </li></ul></ul></ul><ul><ul><ul><li>New browsing capabilities for news stories (Pouliquen et al, 2007; Tanev, 2007) </li></ul></ul></ul><ul><ul><li>A more secured/easy way to share private content with trusted individuals </li></ul></ul><ul><ul><ul><li>“ Web of Trust” (Golbeck, 2008; Matsuo et.al., 2004) </li></ul></ul></ul>Why Do We Want To Discover Online Social Networks?
    4. 4. <ul><li>Companies </li></ul><ul><ul><li>Recruiting talents </li></ul></ul><ul><ul><ul><li>Different ties for different needs (Leung, 2003) </li></ul></ul></ul><ul><ul><li>Finding experts </li></ul></ul><ul><ul><ul><li>Expertise oriented searching using social networks (Ehrlich et al, 2007; Li et al, 2007) </li></ul></ul></ul><ul><ul><li>Marketing </li></ul></ul><ul><ul><ul><li>Viral marketing (Domingos, 2005) </li></ul></ul></ul><ul><ul><ul><li>Building brand loyalty using customer networks (Thompson & Sinha, 2008) </li></ul></ul></ul>Why Do We Want To Discover Online Social Networks?
    5. 5. <ul><li>Researchers </li></ul><ul><ul><li>Ability to ask and answer deeper questions about the nature and operation of online communities </li></ul></ul><ul><ul><ul><li>How and why one online community emerges and another dies? </li></ul></ul></ul><ul><ul><ul><li>How people agree on common practices and rules in an online community? </li></ul></ul></ul><ul><ul><ul><li>How knowledge and information is shared among group members? </li></ul></ul></ul>Why Do We Want To Discover Online Social Networks?
    6. 6. Case Study: Online Communities Among Blog Readers <ul><li>Can a blog support the development of an online community? </li></ul><ul><li>How do we know if a community has emerged among blog readers? </li></ul>
    7. 7. 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 ?
    8. 8. Outline <ul><li>Why Do We Want To Discover Online Social Networks? </li></ul><ul><li>How Do We Collect Information About Social Networks? </li></ul><ul><li>A Case Study of a Canadian Real Estate Blog </li></ul><ul><li>Future Research </li></ul>
    9. 9. Automated Discovery of Social Networks Name Network Approach 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. Ann -> Steve Ann -> Natasha Connect the sender to people mentioned in the message Steve <-> Natasha Connect people whose names co-occur in the same message(s) Discovered Tie(s) Method
    10. 10. <ul><li>Main Communicative Functions of Personal Names (Leech, 1999) </li></ul><ul><ul><li>getting attention and identifying addressee </li></ul></ul><ul><ul><li>maintaining and reinforcing social relationships </li></ul></ul><ul><li>Names are “one of the few textual carriers of identity” in discussions on the web (Doherty, 2004) </li></ul><ul><li>Their use is crucial for the creation and maintenance of a sense of community (Ubon, 2005) </li></ul>Automated Discovery of Social Networks Name Network Approach
    11. 11. ICTA - Online Tool for Social Network Discovery http://TextAnalytics.net
    12. 12. Outline <ul><li>Why Do We Want To Discover Online Social Networks? </li></ul><ul><li>How Do We Collect Information About Social Networks? </li></ul><ul><li>A Case Study of a Canadian Real Estate Blog </li></ul><ul><li>Future Research </li></ul>
    13. 13. Case Study: Online Communities Among Blog Readers <ul><li>Can a blog support the development of an online community? </li></ul><ul><li>How do we know if a community has emerged among blog readers? </li></ul>
    14. 14. Characteristics of Online Community <ul><li>Virtual Settlement (Jones, 1997) </li></ul><ul><ul><li>virtual common-public-place </li></ul></ul><ul><ul><li>interactivity </li></ul></ul><ul><ul><li>sustained membership </li></ul></ul><ul><li>Sense of Community ( McMillan & Chavis , 1986) </li></ul><ul><ul><li>feelings of membership & influence </li></ul></ul><ul><ul><li>reinforcement of needs </li></ul></ul><ul><ul><li>shared emotional connection </li></ul></ul>
    15. 15. Comments Posted by Blog Readers
    16. 16. Changes in Social Networks over Time January 2009 October 2008 383 # ties 198 (50) # posters 1526 # msg 999 # ties 434 (88) # posters 3217 # msg
    17. 17. SNA Statistics <ul><li>the posters became more connected and more of them took a stand in a group </li></ul>Betweenness Centrality Degree Centrality # Active Posters 31 3 18 7 88 50 January 2009 October 2008
    18. 18. Semi-automated Content Analysis with ICTA
    19. 19. Results: Characteristics of Online Community <ul><li>Sustained membership </li></ul><ul><ul><li>44% (Oct 2008 -> January 2009) </li></ul></ul><ul><li>Highly interactive discussions </li></ul><ul><ul><li>1 in 3 comments directly addresses or references a fellow poster </li></ul></ul><ul><li>Interactions that are important in developing stronger connections between people </li></ul><ul><ul><li>information sharing, help, humor, and support </li></ul></ul><ul><ul><li>“ Sorry to hear about your particular financial situation” </li></ul></ul><ul><li>Self-moderation </li></ul><ul><ul><li>“ whatever you call the matter between your two. Please refrain from bad language” </li></ul></ul><ul><ul><li>“ try to hear what people are saying, and not twist their words” </li></ul></ul><ul><li>M utual awareness </li></ul><ul><ul><li>“ watch out for our resident <Nickname>” </li></ul></ul><ul><ul><li>“ what happened to <Nickname>?” </li></ul></ul>
    20. 20. Conclusions <ul><li>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 </li></ul><ul><li>Even in a blog dominated by mostly anonymous and argumentative commentators, a community can still be formed and strengthened </li></ul><ul><li>Community norms provide blog readers with a “safe” environment to debate different opinions with fellow blog readers. </li></ul>
    21. 21. Outline <ul><li>Why Do We Want To Discover Online Social Networks? </li></ul><ul><li>How Do We Collect Information About Social Networks? </li></ul><ul><li>A Case Study of a Canadian Real Estate Blog </li></ul><ul><li>ICTA - Online Tool for Social Network Discovery </li></ul><ul><li>Future Research </li></ul>
    22. 22. Future Research <ul><li>Collaborators: Caroline Haythornthwaite (University of Illinois at Urbana-Champaign, USA) and Mark Chignell (University of Toronto, Canada) </li></ul><ul><li>This project addresses the question of “What to do with a million blogs?” </li></ul><ul><ul><li>Why and how people maintain existing and/or form new social relationships in the blogosphere? </li></ul></ul><ul><ul><li>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? </li></ul></ul><ul><ul><li>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? </li></ul></ul><ul><ul><li>(under review) </li></ul></ul>Digging Into The Blogosphere: Automated Discovery, Visualization and Evaluation of Social Networks Among Bloggers and Blog Readers
    23. 23. Related Research <ul><li>Networked Learning </li></ul><ul><ul><li>Gruzd, A. (2009). Studying Collaborative Learning Using Name Networks. Journal for Education in Library and Information Science 50(4): 243-253. </li></ul></ul><ul><ul><li>Haythornthwaite, C. and Gruzd, A. (2008). Analyzing Networked Learning Texts. In the Proceedings of Networked Learning Conference , Halkidiki, Greece. </li></ul></ul>
    24. 24. Related Research (cont.) <ul><li>How is Twitter Changing the Ways Scholars Disseminate Knowledge and Information? </li></ul><ul><ul><li>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. </li></ul></ul><ul><ul><li>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. </li></ul></ul>
    25. 25. Related Research (cont.) <ul><li>Korean Internet Network Miner (KINM) </li></ul><ul><ul><li>Analysis of Korean blogging communities on the world-first citizen journalism site OhMyNews (http://www.ohmynews.com). </li></ul></ul><ul><li>Collaborators </li></ul><ul><ul><li>Han-Woo Park (Yeungnam University, S.Korea) </li></ul></ul>
    26. 26. References / Related Work <ul><li>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. </li></ul><ul><li>Chin, A. and M. Chignell (2007). &quot;Identifying Communities in Blogs: Roles for Social Network Analysis and Survey Instruments.&quot; International Journal of Web Based Communities 3(3): 343-365 </li></ul><ul><li>Domingos, P. (2005). &quot;Mining social networks for viral marketing.&quot; IEEE Intelligent Systems 20(1): 80-82. </li></ul><ul><li>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. </li></ul><ul><li>Espinoza, F., P. Persson, et al. (2001). GeoNotes : Social and Navigational Aspects of Location-Based Information Systems. Ubicomp 2001: Ubiquitous Computing: 2-17. </li></ul><ul><li>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. </li></ul><ul><li>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. </li></ul><ul><li>Golbeck, J. (2008). &quot;Trust and Nuanced Profile Similarity in Online Social Networks.&quot; ACM Transactions on the Web. </li></ul><ul><li>Golbeck, J. (2008). &quot;Trust and Nuanced Profile Similarity in Online Social Networks.&quot; ACM Transactions on the Web. </li></ul><ul><li>Haythornthwaite, C. (2006). &quot;Facilitating collaboration in online learning.&quot; Journal of Asynchronous Learning Networks 10(1): 7-24. </li></ul><ul><li>Jones, Q. (1997). Virtual Communities, Virtual Settlements And Cyber-Archaeology. Journal of Computer Mediated Communication 3(3). </li></ul><ul><li>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. </li></ul><ul><li>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 </li></ul>
    27. 27. References / Related Work (cont.) <ul><li>Leung, A. (2003). &quot;Different ties for different needs: Recruitment practices of entrepreneurial firms at different developmental phases.&quot; Human Resource Management 42(4). </li></ul><ul><li>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. </li></ul><ul><li>Matsuo, Y., H. Tomobe, et al. (2004). Finding Social Network for Trust Calculation. the 16th European Conference on Artificial Intelligence (ECAI2004). </li></ul><ul><li>Matsuo, Y., H. Tomobe, et al. (2004). Finding Social Network for Trust Calculation. the 16th European Conference on Artificial Intelligence (ECAI2004) 16: 510. </li></ul><ul><li>McMillan, D.W. & Chavis, D.M. (1986). Sense of community: A definition and theory. Journal of Community Psychology 14(1): 6-23. </li></ul><ul><li>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. </li></ul><ul><li>Savignon, S.J. and Roithmeier, W. (2004). Computer-Mediated Communication: Texts and Strategies. Computer Assisted Language Instruction Consortium Journal 21(2): 265-290. </li></ul><ul><li>Swearingen, J. (2008). Four Ways Social Networking Can Build Business. Bnet.com. Retrieved from http://www.bnet.com/2403-13070_23-219914.html </li></ul><ul><li>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. </li></ul><ul><li>Thompson, S. A. and R. K. Sinha (2008). &quot;Brand Communities and New Product Adoption:The Influence and Limits of Oppositional Loyalty.&quot; Journal of Marketing 72(6): 65-80. </li></ul>
    28. 28. Automated Discovery of Emerging Online Communities Among Blog Readers: A Case Study of a Canadian Real Estate Blog Anatoliy Gruzd [email_address] October 11, 2009 “ C omputer networks are inherently social networks, linking people, organizations, and knowledge” Wellman (2001)
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