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Session 5C [clear filter]
Wednesday, July 13
 

13:45

Session 5C: Networks
Location: PSH (Professor Stuart Hall Building) - 314, 
Goldsmiths, University of London, Building 2
Campus Map 


Moderators
avatar for Caroline Haythornthwaite

Caroline Haythornthwaite

Professor, UBC
Caroline Haythornthwaite is Professor in The iSchool at The University of British Columbia to June 2016. She is joining the Syracuse University iSchool in August 2016. Areas of interest: social network perspective applied to questions about online organizing (notably about online... Read More →

Wednesday July 13, 2016 13:45 - 15:15
PSH (Professor Stuart Hall Building) - 314 Goldsmiths University, Building 2

13:46

Audience Brokers and Content Discoverers in the Networked Public Sphere
Location: PSH (Professor Stuart Hall Building) - 314, 
Goldsmiths, University of London, Building 2
Campus Map 

Contributors:
  • Silvia Majo-Vazquez, Internet Interdisciplinary Institute - Universitat Oberta de Catalunya, Spain
  • Ana S. Cardenal, Universitat Oberta de Catalunya -Internet Interdisciplinary Institute, Spain
  • Oleguer Sagarra Pascual, Complex Systems Group, Física Fonamental - University of Barcelona, Spain
  • Pol Colomer de Simón, Complex Systems Group, Física Fonamental - University of Barcelona, Spain

Background:

Social platforms such as Facebook or Twitter as well as news aggregators and blogs are changing the way people consume news online. Citizens are less willing to search political content directly from branded websites (N. Newman, Levy, & Nielsen, 2015) yet they increasingly relay on their news feeds to bump into political information. Much has been discussed about the potential and the consequences of the mediatization role of these new players (Gitlin, 2002; Napoli, 2008; Pariser, 2011; Prior, 2008; Sunstein, 2009; Turow, 1998) but little empirical evidence has been brought so far. The purpose of this study is to offer some redress to the situation by analyzing the role of new media building on the network of news audience and the news providers. 

Objective: 


Our study seeks to empirically assess the potential of new media as audience distributors and content discoverers. We want to know whether they promote audience flow and hence, they help to avoid the balkanization of the web (Sunstein, 2009). Our hypothesis falls in line with theoretical accounts contending that audiences do not form enclaves in the online domain but they share a public realm (Garrett & Resnick, 2011; Gentzkow & Shapiro, 2011; Webster & Ksiazek, 2012).

Methods: 


We make explicit the concept of the online public domain by building two networks: the network of audience and the network of online news providers. The former comes from a sample of 113 news media, new and traditional outlets in Spain and represents the audience that each pair of news providers sent to each other (Webster & Ksiazek, 2012). The network of online news providers is built using a hyperlink crawling (Ackland, 2013) starting from a list of 44 seed sites corresponding to the most visited news media outlets. We test the role of new media as audience brokers (Gould & Fernandez, 1989). We use the random-walk betweenness centrality (Newman, 2005) -which counts not only the shortest paths but all paths between two nodes- to test the influence of a node over the spread of audience. Finally, we assess the potential of new media to discover authoritative news sources following the empirical framework proposed by Kleinberg, (1999). 

Results: 


Using second order methods in network science (Borge-Holthoefer & Gonzalez-Bailon, 2015), we aim to bring evidence on firstly, that new media provides shortcuts that decrease significantly the number of hops that audience must take to explore a broader range of news content; secondly, that they have the potential to diversify news media diets by discovering new sources of information. In our study we also shed light on the role of legacy media as authorities in the online domain.  

Future Work: 


Despite the access to news is still dominated by television, people increasingly access media content just login into their social media platforms and visiting feeds readers. This is an increasingly common habit in US, Ireland and Australia (N. Newman et al., 2015). Hence, in future research we aim to enhance the present analysis focused on Spain by bringing a comparative perspective based on theses countries. 

References:

Ackland, R. (2013). Web Social Science Concepts, Data and Tools for Social Scientists in the Digital Age (1st ed.). London: SAGE Publications Ltd.

Borge-Holthoefer, J., & Gonzalez-Bailon, S. (2015). Scale, Time, and Activity Patterns: Advanced Methods for the Analysis of Online Networks. In N. Fielding, R. Lee, & G. Blank (Eds.), Handbook of Online Research Methods (Second). Thousand Oaks: Sage Publications. Retrieved from http://ssrn.com/abstract=2686703

Garrett, R. K., & Resnick, P. (2011). Resisting political fragmentation on the Internet. Daedalus, 140(4), 108–120.

Gentzkow, M., & Shapiro, J. M. (2011). Ideological Segregation Online and Offline. The Quarterly Journal of Economics, 126(4), 1799–1839. http://doi.org/10.1093/qje/qjr044

Gitlin, T. (2002). Public sphere or public sphericules? In J. Curran & T. Liebes (Eds.), Media, ritual and identity (p. 168). Routledge.

Gould, R. V, & Fernandez, R. M. (1989). Formal Approach to Brokerage in Transaction Networks. Sociological Methodology, 19, 89–126.

Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM (JACM), 46(5), 604–632.

Napoli, P. M. (2008). Toward a model of audience evolution: New Tecnologies and the Transformation of Media Audiences (No. 15). Retrieved from http://fordham.bepress.com/mcgannon_working_papers/15

Newman, M. E. J. (2005). A measure of betweenness centrality based on random walks. Social Networks, 27(1), 39–54.

Newman, N., Levy, D. A., & Nielsen, R. K. (2015). Reuters Institute Digital News. Report 2015. Tracking the future of news. Retrieved from https://reutersinstitute.politics.ox.ac.uk/sites/default/files/Reuters Institute Digital News Report 2015_Full Report.pdf

Pariser, E. (2011). The filter bubble: How the new personalized web is changing what we read and how we think. Penguin.

Prior, M. (2008). Are hyperlinks “weak ties”? In J. Turow & L. Tsui (Eds.), The hyperlinked society: questioning connections in the Digital Age (pp. 227–249). University of Michigan Press Ann Arbor, MI.

Sunstein, C. R. (2009). Republic. com 2.0 (second). Princeton University Press.
Turow, J. (1998). Breaking up America: Advertisers and the new media world. University of Chicago Press.

Webster, J. G., & Ksiazek, T. B. (2012). The Dynamics of Audience Fragmentation: Public Attention in an Age of Digital Media. Journal of Communication, 62(1), 39–56. http://doi.org/10.1111/j.1460-2466.2011.01616.x 


Wednesday July 13, 2016 13:46 - 15:15
PSH (Professor Stuart Hall Building) - 314 Goldsmiths University, Building 2

13:46

Insiders or outsiders? The hidden network of sustaining online community
Location: PSH (Professor Stuart Hall Building) - 314, 
Goldsmiths, University of London, Building 2
Campus Map 

Contributors:
  • Joyce Lee, Yuan-Ze University, Taiwan
  • Shu-Fen Tseng, Yuan-Ze University, Taiwan
  • Chih-Yao Chang, Dharma Drum Institute of Liberal Arts, Taiwan
  • K. Robert Lai, Yuan-Ze University, Taiwan
  • Shih-Yun Chen, Yuan-Ze University, Taiwan
  • Lu Shi, Huazhong University of Science and Technology, CHina

Background: 

The explosion of social media has changed the ways people communicate and interact. In particular, online communities have enabled people to find the others with common interests, passions, or problems over the Internet, and subsequently to share information, feelings as well give mutual supports. By doing so, online communities can be significantly valuable to the people who use them and hence become a great benefit to society which is regarded as important social capital across multiple dimensions. Despite their increasing value to the society, researchers have noticed that relatively few of them are successful in attracting community members and enhancing interactivity (Phang et al., 2009). To tackle this challenge, we argue that it is necessary to have an in-depth understanding regarding online participation in the communal contexts.

Objective: 

From the perspective of online community sustainability, prior researchers have involved investigating the participation roles along with their particular behaviour (e.g. Preece & Schneiderman, 2009) so that their influences in user interactivities in communities can be understood. Whilst these researchers have shed significant light on this domain, the participation patterns pertaining to both the participation roles and the content of message that constantly intertwine in sustaining community activities have received little attention. We argue that in online communities especially those with particular purposes, the content of posts is one of the drivers of user participation in conversations or discussions, and that consequentially leads to different typologies of content networks to emerge (Kane et al., 2014) as well as facilitating networked communication and user interactivities (van Varik & van Oostendorp, 2013).This is important, because people in an online community are not only connected to other people, for they are also connected to the content, which can be connected to other content (Oestricher-Singer & Zalmanson, 2013). This study, by drawing upon the theory of online participation, aims to understand participation patterns consist of roles and content emerged in the community contexts and thereby increasing the value of online knowledge sharing.

Methods: 

For this research, a mixed method of social network analysis with both qualitative and quantitative strategies has been undertaken on the discussion forum URcar (a pseudonym). Specifically, the discussion topic about the vehicle model Nissan Cefiro entitled “Cefiro’s owners, please come to sign here” is selected as the main case study (not least) for it being the longest car-related discussion, for the period February 2007 to November 2015 (lasting for nine years and still active) and thus can be considered as an online community with sustainability. The development of this long-lasting discussion topic offers a great opportunity for the study of the users’ dynamic behaviour in a communal context.

Results: 

The findings reveal that: first, in online communities with an open conversation space, an individual can participate in a central role in some circumstances, but in a peripheral way in others. Thus, we argue that participation roles are not a signature of the person but a contextual behaviour that has its distinctive social meanings. Second, by exploring the actor-content networks, we found that the “main channels” within which some participants discussed the issues directly related to the car model and the “side channels” within which participants developed their social relationships by talking about something else. In fact, the co-existence of main and side channels led the community being active.

Future Work: 

In this research, we redefine the meaning of community contribution. Specifically, through reinterpretation of centrality and de-centrality in networks, we have uncovered the hidden influences which contribute the community in knowledge sharing. That is, by considering the message content as well as the role of offline communication alongside the online form, we have identified some people who have a strong impact on maintaining online community sustainability, despite their lack of posting. In order to reach a rigour level of research, more cases are studied continuously.

References:

Kane, G., Alavi, M., Labianca, G. J. and Borgatti, S. P. (2014). What's different about social media networks? A framework and research agenda. MIS Quarterly, 38, 275-304.

Oestricher-Singer, G. and Zalmanson, L. (2013). Content or Community? A Digital Business Strategy for Content Providers in The Social Age. MIS Quarterly, 37, 591-616.

Phang, C. W., Kankanhalli, A. and Sabherwal, R. (2009). Usability and Sociability in Online Communities: A Comparative Study of Knowledge Seeking and Contribution. Journal of the Association for Information Systems, 10, 721-747.

Preece, J. and Schneiderman, B. (2009). The Reader-to-Leader Framework: Motivating Technology-Meditated Social Participation. AIS Transactions on Human-Computer Interaction, 1, 13-32.

van Varik, F. J. M. and van Oostendorp, H. (2013). Enhacning Online Community Activity: Development and validation of the CA framework. Journal of Computer-Mediated Communication, 18, 454-475.



Wednesday July 13, 2016 13:46 - 15:15
PSH (Professor Stuart Hall Building) - 314 Goldsmiths University, Building 2

13:46

One Day in the Life of a National Twittersphere
Location: PSH (Professor Stuart Hall Building) - 314, 
Goldsmiths, University of London, Building 2
Campus Map 

Contributors:
  • Axel Bruns, Queensland University of Technology, Australia
  • Brenda Moon, Queensland University of Technology, Australia

Background: 

Much of the existing research into the uses of social media platforms focusses on the exceptional: key moments in politics (e.g. Larsson & Moe, 2014; Sauter & Bruns, 2015, Papacharissi & Blasiola, 2016), sports (e.g. Blaszka et al., 2012; Highfield, 2014), brand management (e.g. Krüger et al., 2012; Nitins & Burgess, 2014), or crisis communication (e.g. Mendoza et al., 2010; Palen et al., 2010; Shaw et al., 2013). For the case of Twitter, because of the way that the Twitter API privileges certain data gathering approaches, such work is usually centred on one or more hashtags or keywords (Burgess & Bruns, 2015). This line of inquiry has produced many useful insights into the uses of Twitter – as documented for example in the collection Hashtag Publics (Rambukkana, 2015) – but arguably it covers only one subset of the various uses of the platform. Routine and everyday social media practices remain comparatively underexamined as a result; for Twitter, therefore, what results is an overrepresentation in the literature of the loudest voices – those users who contribute actively to popular hashtags.

Objective: 

This paper presents progress results from a major new study that examines user activity patterns on Twitter well beyond limited hashtag collections, drawing on a comprehensive dataset that tracks the public activities of all Twitter accounts identified by their profile information as Australian. Building on this cohort (currently containing some 2.8 million accounts), we have already mapped the follower/followee relationships within the Australian Twittersphere (Bruns et al., 2014) to identify the clustering patterns that influence – arguably more so than the use of hashtags – how information flows between users. We have also identified the thematic drivers of cluster formation in the network, and have mapped participation in specific Twitter conversations across these clusters.

The paper builds on this earlier work by exploring in depth the day-to-day patterns of activity within the Australian Twittersphere, for a selection of several 24-hour periods during 2015. This provides a unique new insight into how, across an entire national Twittersphere, conversations between users unfold through the day, and documents the extent to which such interactions are guided by existing follower relationships, hashtags, or other contextual markers. Inter alia, our analysis will show which parts of the network are consistently active across all periods, and which are triggered by the events of the day; which are more focussed on publishing new content (through original tweets), on interpersonal conversation (through @mentions), or on news dissemination (through retweets); and which are influential across the network, or remain largely within their own clusters. What is revealed through this work is a largely hidden side of Twitter away from the prominent hashtags.

Methods: 

Our comprehensive dataset of the public tweets by some 2.8 million identified Australian accounts enables filtering by the timestamps of tweets. We select several 24-hour periods across 2015 (averaging between 900,000 and 1 million tweets per day), to capture all tweets by Australian users during these days; we then extract from the tweet text any @mentions and retweets of other users, and generate a network map of their interactions to examine the processes of interpersonal engagement between these accounts. In doing so, we determine the properties of this network (and how they change through the course of the day), and examine the extent to which @mention or retweet engagement is a feature of overall activity in the Australian Twittersphere at any one point (that is, to what extent users are simply tweeting undirected personal statements, talk with each other through @mentions, or share other accounts’ posts through retweets). We also correlate this with the network clusters we have already identified in the follower network, to explore whether specific practices (posting, @mentioning, retweeting) are more prevalent in particular clusters of the network.

Results: 

The outcomes from this study provide new insights into the dynamics of Twitter engagement well beyond well-understood phenomena such as hashtags. They shed new light on how everyday users utilise Twitter, and document the degree of diversity of the personal networks they actively engage with.

Future Work: 

We focus here in the first place on the documentation of comparatively ordinary days in the Australian Twittersphere. Future work will compare such patterns with extraordinary periods (such as major political, media, or sporting events), to explore how and to what extent the routine patterns of Twitter engagement change as breaking news disrupts users’ activities. Additionally, the network analysis presented here will also be combined with automated textual analysis, to examine whether changes in activity patterns through the day are correlated with thematic shifts in the content of the tweets.

References:

Blaszka, M., Burch, L.M., Frederick, E.L., Clavio, G., & Walsh, P. (2012). #WorldSeries: An Empirical Examination of a Twitter Hashtag during a Major Sporting Event. International Journal of Sport Communication5(4): 435-453.

Bruns, A., Burgess, J., & Highfield, T. (2014). A “Big Data” Approach to Mapping the Australian Twittersphere. In P.L. Arthur & K. Bode (Eds.), Advancing Digital Humanities: Research, Methods, Theories (pp. 113–129). Houndmills: Palgrave Macmillan.

Burgess, J., & Bruns, A. (2015). Easy Data, Hard Data: The Politics and Pragmatics of Twitter Research after the Computational Turn. In G. Langlois, J. Redden, & G. Elmer (Eds.), Compromised Data: From Social Media to Big Data (pp. 93–111). New York: Bloomsbury Academic.

Highfield, T. (2014). Following the Yellow Jersey: Tweeting the Tour de France. In K. Weller et al., (Eds.), Twitter and Society (pp. 249-262). New York: Peter Lang.

Krüger, N., Stieglitz, S., & Potthoff, T. (2012). Brand Communication in Twitter: A Case Study on Adidas. In PACIS 2012 Proceedings (paper 161). Retrieved from http://aisel.aisnet.org/pacis2012/161.

Larsson, A.O., & Moe, H. (2014). Twitter in Politics and Elections: Insights from Scandinavia. In K. Weller et al., (Eds.), Twitter and Society (pp. 319-330). New York: Peter Lang.

Mendoza, M., Poblete, B., & Castillo, C. (2010). Twitter under Crisis: Can We Trust What We RT? In Proceedings of the First Workshop on Social Media Analytics (SOMA ’10) (pp. 71-79). Retrieved from http://snap.stanford.edu/soma2010/papers/soma2010_11.pdf.

Nitins, T., & Burgess, J. (2014). Twitter, Brands, and User Engagement. In K. Weller et al., (Eds.), Twitter and Society (pp. 293-303). New York: Peter Lang.

Palen, l., Starbird, K., Vieweg, S., & Hughes, A. (2010). Twitter-Based Information Distribution during the 2009 Red River Valley Flood Threat. Bulletin of the American Society for Information Science and Technology, 36(5): 13-17.

Rambukkana, N. (Ed.). (2015). Hashtag Publics: The Power and Politics of Discursive Networks. New York: Peter Lang.

Papacharissi, Z., & Blasiola, S. (2016). Structures of Feeling, Storytelling, and Social Media: The Case of #Egypt. In Bruns et al. (Eds.), The Routledge Companion to Social Media and Politics (pp. 211-222). London: Routledge.

Sauter, T., & Bruns, A. (2015). #auspol: The Hashtag as Community, Event, and Material Object for Engaging with Australian Politics. In N. Rambukkana (Ed.), Hashtag Publics: The Power and Politics of Discursive Networks (pp. 47–60). New York: Peter Lang.

Shaw, F., Burgess, J., Crawford, K., & Bruns, A. (2013). Sharing News, Making Sense, Saying Thanks: Patterns of Talk on Twitter during the Queensland Floods. Australian Journal of Communication, 40(1), 23-39. Retrieved from http://search.informit.com.au/documentSummary;dn=430834117446976;res=IELHSS.



Wednesday July 13, 2016 13:46 - 15:15
PSH (Professor Stuart Hall Building) - 314 Goldsmiths University, Building 2

13:46

Social Network Structure of Online Communities: Social Movement Activists, Professionals and Fans on VK.com SNS
Location: PSH (Professor Stuart Hall Building) - 314, 
Goldsmiths, University of London, Building 2
Campus Map 

Contributor: Yuri Rykov, National Research University Higher School of Economics, Russian Federation

Background:

Becoming Web 2.0 era (O'Reilly, 2005) associated with the popularization of social network sites (SNS) updates the sociological debate over the concept of online communities that exist on these platforms (Cavanagh, 2009). These online communities are used for very different purposes: to find likeminded others, for professional knowledge sharing, organizing protest events and other civil society activity, education, in public health, etc. SNS-based online communities relating to social groups from various spheres of social life probably differ from each other by participant's networking and communication behavior. The structure and users' interaction patterns within online communities vary depending on the platform technical features, temporal structure, external contexts (e.g. language), participants characteristics and group purposes (Baym, 1998; Preece, 2001; Gonzalez-Bailon, Kaltenbrunner, & Banchs, 2010). Recent research devoted to study of discussion communities in Twitter shows there is a relation between structural patterns of discussion networks and subjects of communication (Smith, Rainie, Shneiderman, & Himelboim, 2014). Therefore, the study of communities' functioning and structure in comparative perspective is of interest. 
In this particular research we focus on online communities form different spheres of social life and with different purposes respectively: fan communities, professional communities, social movement communities. 

Objective:

The research question is what are the differences between 'friendship' networks of these three types of online communities. The answer shed light on how purposes of online communities determine forms of connectivity and collective behavior within. 

Methods: 

An empirical object are online groups in the most popular Russian SNS VK.com. Sample includes 55 groups (vary in size from 5,000 to 34,000 users) equally corresponding to three exploring types of communities: fan communities (e.g. musicians fans), professional communities (e.g. IT specialists, engineers) and social movement communities (e.g. urban, LGBT movements). 
The data was available through API and was collected automatically by special software. Each group dataset includes: 1) complete data from group's 'wall' and discussion boards including users' activity stats; 2) the metadata of all participants (gender, age, location, etc); 3) the data on 'friend' relationships existing among community participants. 
Nodes in the network are users participating in online groups. Ties are 'friend' relationships between them. To analyze data we use social network analysis methods and statistics (linear models, ANOVA). 

Results: 

Fan networks have lower density and are less filled with ties, comparing to other groups. Fan networks have significantly more connected components and graph clusters, a higher value of Gini index for betweenness centrality distribution, indicating a greater fragmentation of fan communities compared with other. It means participants are less likely to use fan groups to networking with like-minded individuals and form a social capital. 
Professional communities have the largest share of posting users and lowest Gini index for posted messages distribution that indicates more participatory behaviour of users in content creation and knowledge sharing. Despite the wide participation professional networks stay highly fragmented and clustered that is caused by the highest betweenness centralization. 
Social movement networks are the most dense and the most internally connected, comparing to others, because the collective action require the cooperation between participants. Despite solidarity and cohesion these networks are the most centralized and unequal by degree centrality. Thus, online communities are used by movement activists to accumulate group-level social capital, but larger inequality emerges on the individual-level social capital. 

Future Work: 

We are going to continue statistical analysis to obtain more results. Also we are planning to conduct a content analysis of these groups using topic modelling approach and techniques. 

References: 

Baym, N. K. (1998). The Emergence of On-Line Community. In S. G. Jones (Ed.), Cybersociety 2.0: Revisiting Computer-Mediated Communication and Community (pp. 35–68). Thousand Oaks, CA: SAGE. 
Cavanagh, A. (2009). From Culture to Connection: Internet Community Studies. Sociology Compass, 3(1), 1–15. http://doi.org/10.1111/j.1751-9020.2008.00186.x 
Gonzalez-Bailon, S., Kaltenbrunner, A., & Banchs, R. E. (2010). The structure of political discussion networks: a model for the analysis of online deliberation. Journal of Information Technology, 25(2), 230–243. http://doi.org/10.1057/jit.2010.2 
O’Reilly, T. (2005, September 30). What is Web 2.0. Design patterns and business models for the next generation of software. Retrieved from http://www.oreilly.com/pub/a/web2/archive/what-is-web-20.html. 
Preece, J. (2001). Sociability and usability in online communities: Determining and measuring success. Behaviour & Information Technology, 20(5), 347–356. http://doi.org/10.1080/01449290110084683 
Smith, M. A., Rainie, L., Shneiderman, B., & Himelboim, I. (2014, February 20). Mapping twitter topic networks: From polarized crowds to community clusters. Pew Research Internet Project. Retrieved from http://www.pewinternet.org/2014/02/20/part-2-conversational-archetypes-six-conversation-and-group-network-structures-in-twitter/ 

Wednesday July 13, 2016 13:46 - 15:15
PSH (Professor Stuart Hall Building) - 314 Goldsmiths University, Building 2