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Session 5D [clear filter]
Wednesday, July 13


Session 5D: Participation
Location: PSH (Professor Stuart Hall Building) - 326, 
Goldsmiths, University of London, Building 2
Campus Map 

avatar for Jill Hopke

Jill Hopke

Assistant Professor, DePaul University
I am an Assistant Professor of Journalism in the College of Communication at DePaul University in Chicago. My work explores the interface of people, the environment, new technologies and social movements.

Wednesday July 13, 2016 13:45 - 15:15
PSH (Professor Stuart Hall Building) - 326 Goldsmiths University, Building 2


Choice shaping in Social Media: An Evolutionary perspective
Location: PSH (Professor Stuart Hall Building) - 326, 
Goldsmiths, University of London, Building 2
Campus Map 

Contributor: Gabriela Morales, The University of Sheffield


The information people choose (in the form of opinions, advice or ideas) determines to a great extent the knowledge they acquire (Barthelme, Ermine, & Rosenthal-Sabroux, 1998; Nonaka, 1994). However, in order to forego the costs of individual learning, people have evolved to acquire knowledge through social learning processes like teaching, language and imitation (Mesoudi, 2011). Specifically, some evolutionary scholars have focused on three biases that take place when grouped individuals interact: they tend to conform to the beliefs of the group (frequency-based); they tend to imitate the ideas of powerful or alike individuals (model-based); or they simply select information that is perceived as having more benefits compared to the other options (content-based), (Mesoudi, 2011; Richerson & Boyd, 2005).

These biases tend to happen whenever people are grouped, but never before have we seen as many individuals interacting as we do now. With almost half of the world’s population making use of the internet (Internet Live Stats, 2015) and given the amount of information that is being shared and received by users, online communities have had to implement different structures that simplify the sharing of information. However, at the same time that these structures simplify and tailor the information we need, they also make us prone to obtaining information that is biased (Kahneman, 2003; Tversky & Kahneman, 1981). These structures affect: the amount of information allowed to be transmitted to other users (maximum or minimum characters allowed per ‘post’), the type of information that can be used (for instance, text, images, or video), the reach within the whole online community (i.e., some opinions are shared only within a selected group of acquaintances while others are meant to be seen by any online user), and the level of conformity towards an idea shared by someone in the network (by making use of different rating-scales).


The main aim of this research will be to analyse how these different structures might bias the information people receive, and to determine which biases have a greater impact at the moment an individual is choosing from available opinions, advice or ideas. To achieve this, the current research done in social media was structured around the three group-biases (content, model, and frequency-based). The literature review showed that research has already being performed regarding what makes information attractive to others in terms of its content (Cheng & Ho, 2015; Cheung & Thadani, 2012; Jalilvand, Esfahani, & Samiei, 2011; Liu & Park, 2015; Park & Nicolau, 2015), and also in terms of online power or expertise (Iyengar, Van den Bulte, & Valente, 2011; Jacobsen, 2015; Litvin, Goldsmith, & Pan, 2008; Wu, Hofman, Mason, & Watts, 2011). However few studies have addressed the topic of conformity in online networks (Tsao, Hsieh, Shih, & Lin, 2015). Particularly, the differentiation between the personal and the total social network has been under-studied (Jiang, Ma, Shang, & Chau, 2014; Luo & Zhong, 2015). Moreover, although some research has also been performed regarding the comparison of rating-scales in online environments (Riedl, Blohm, Leimeister, & Krcmar, 2010, 2013), these studies have not differentiated personal networks within the online platforms. 

Therefore, to address these gaps, the present study explores the following research questions: Which are the biases that mostly affect online choices? How strongly does conformity affect the choices in social media? Do people conform differently to the total-network than to their online personal-networks? Does the selection of information from someone’s personal network get affected by two different rating- systems?


To target the research questions, the study will adopt a quasi-experimental approach, and the data gathered will be both quantitative and qualitative, and longitudinal in nature. The quasi-experiment will consist of three years of data generated within an online educational website (PeerWise) where the participants will be (non-randomly allocated) undergraduate students of a particular module in the University of Sheffield. This module currently uses PeerWise throughout the semester, where students use it “to create [multiple choice questions] and to explain their understanding of course-related assessment questions and to answer and discuss questions created by their peers" (PeerWise, 2015).

The study will encompass three years of data: [1] The first year will have the characteristic that Peerwise users will be able to choose their usernames1 and rate2 each other’s questions from 0 to 5. [2] In the second year the change that will take place is that anonymity will be removed. This is, all students will be signed-in with their first and last names3, while the rating-scale continue to be 0 to 5. [3] Finally, during the third year students will continue to be logged-in with their first and last names, and the change will be that the rating scale will go from 0-5 to 0-1 (similar to a ‘like/dislike’).

Each year of the quasi-experiment will have 350 students (approx.) which will generate around 54,000 interactions4. This data will be analysed using statistical methods5. Moreover, at the end of each semester students will be asked to complete a questionnaire where their personal networks (within the group) will be mapped. The data from the questionnaire will then be compared with the way users interacted in PeerWise, using social network 6and sequence7 analyses. Finally, yearly focus groups will be used to get additional qualitative data that helps the researcher better understand the opinions and feelings of participants regarding the presence of their personal networks in online environments and the use of a particular rating scale. 


Theoretically, this research will add value by addressing the previously outlined research questions. Empirically, the research will create value by performing a real-life quasi-experiment which will enable to study conformity to personal-networks and comparison of online rating-scales with a novel methodology. Regarding practice and policy, it will help to better understand the application of social media to education, by studying which structures better enable students to obtain information and retain knowledge.

Future Work

This study is part of an ongoing Ph.D. At the time of the conference the researcher will be performing the first between-group comparison, and will therefore be able to comment on some of the preliminary results.


Barthelme, F., Ermine, J.-L., & Rosenthal-Sabroux, C. (1998). An architecture for knowledge evolution in organisations. European Journal of Operational Research, 109, 414–427. http://doi.org/10.1016/S0377-2217(98)00067-8

Cheng, Y., & Ho, H. (2015). Social influence’s impact on reader perceptions of online reviews. Journal of Business Research, 68(4), 883–887. http://doi.org/10.1016/j.jbusres.2014.11.046

Cheung, C. M. K., & Thadani, D. R. (2012). The impact of electronic word-of-mouth communication: A literature analysis and integrative model. Decision Support Systems, 54(1), 461–470. http://doi.org/10.1016/j.dss.2012.06.008

Elzinga, C. H., & Studer, M. (2015). Spell Sequences, State Proximities, and Distance Metrics. Sociological Methods & Research, 44(1), 3–47. http://doi.org/10.1177/0049124114540707
Granovetter, M. S. (1973). The Strength of Weak Ties. American Journal of Sociology, 78(6), 1360. http://doi.org/10.1086/225469

Internet Live Stats. (2015). Internet Users. Retrieved May 18, 2015, from http://www.internetlivestats.com/internet-users/

Iyengar, R., Van den Bulte, C., & Valente, T. W. (2011). Opinion Leadership and Social Contagion in New Product Diffusion. Marketing Science, 30(2), 195–212. http://doi.org/10.1287/mksc.1100.0566

Jackson, M. O. (2008). Social and economic networks. Princeton, N.J.: Princeton University Press.
Jacobsen, G. D. (2015). Consumers, experts, and online product evaluations: Evidence from the brewing industry. Journal of Public Economics, 126, 114–123. http://doi.org/10.1016/j.jpubeco.2015.04.005

Jalilvand, M. R., Esfahani, S. S., & Samiei, N. (2011). Electronic word-of-mouth: Challenges and opportunities. Procedia Computer Science, 3, 42–46. http://doi.org/10.1016/j.procs.2010.12.008 

Jiang, G., Ma, F., Shang, J., & Chau, P. Y. K. (2014). Evolution of knowledge sharing behavior insocial commerce: An agent-based computational approach. Information Sciences, 278, 250–266.http://doi.org/10.1016/j.ins.2014.03.051

Kahneman, D. (2003). A Perspective on Judgment and Choice: Mapping Bounded Rationality. American Psychologist, 58(9), 697–720. http://doi.org/10.1037/0003-066X.58.9.697

Litvin, S. W., Goldsmith, R. E., & Pan, B. (2008). Electronic word-of-mouth in hospitality and tourism management. Tourism Management, 29(3), 458–468. http://doi.org/10.1016/j.tourman.2007.05.011

Liu, Z., & Park, S. (2015


Wednesday July 13, 2016 13:46 - 15:15
PSH (Professor Stuart Hall Building) - 326 Goldsmiths University, Building 2


Click Here: 'Slacktivism' and the Question of Commitment
Location: PSH (Professor Stuart Hall Building) - 326, 
Goldsmiths, University of London, Building 2
Campus Map 

  • Chandell Gosse, Western University, Canada
  • Anabel Quan Haase, Western University, Canada
  • Alyssa MacDougall, Western University, Canada

Background: This project examines the popular term “slacktivism” through an investigation of social media users’ participation in social movements online and offline. Ensuring the success of social movements is an impossible task because their success, i.e., social or political change that occurs as a result of organized efforts to do so, rests on a complex matrix of conditions (e.g., social, economic, political, personal, economic, geographical, etc.). A key feature known to be effective, at least, involves perseverance and long-term commitment (Corrigall-Brown 2011). The growth of social media as a popular communication platform for the awareness and organization of many social movements, called here social and political online campaigns (SPOCs), has led to widespread debate over the merits of its use in relation to such perseverance by dismissing involvement as “slacktivism” or simply a “feel good measure.” Beyond the campaign’s number of likes, posts, tweets, and monetary donations, however, there is little empirical data on which to form an opinion. Understanding the new shift from offline to online communication within social movements and their related SPOCs requires knowing more than simply how many people participated, or how popular the campaign appeared to be. As such, our project asks whether participation in social and political online campaigns is a determinate factor for participation outside of a social media context. 

Objective: Given the emphasis on long-term commitment, and the short-shelf life of many online campaigns, our project seeks to understand whether individuals who participate in SPOCs continue to support the causes behind the campaigns outside of the social media sphere. Our project has two objectives: first, to determine whether SPOCs mobilize action outside the sphere of social media; and second, to determine whether such action continues after the campaigns recede from social media spotlight. Our central argument states that even though participation occurs by a few highly engaged individuals, the long tail of participation—that is, the moderately engaged majority—still comprises a significant portion of total engagement. We rely on Anderson’s (2006) theory of the long tail to better understand the phenomenon of engagement in viral campaigns. 

Methods: This research comes at a crucial time; as technology continues to evolve and the ubiquity of social media as a primary form of communication increases, it is important to understand how people from all walks of life engage with online activism. Our project draws from participants’ responses to surveys advertised on the social networking sites Reddit, Twitter, and Facebook. The survey is comprised of open-ended and close-ended questions and is available to anyone over the age of eighteen. 

Results: The online survey (found here: https://goo.gl/AmQiMM) will remain active until March 2016. The results from this survey are expected to provide insight into four main areas of interest: a) why social media users’ choose to participate in SPOCs and what that participation consists of; b) whether social media users’ learn about issues pertaining to the SPOCs they participate in outside of the social media context, and if so, what the breadth of that knowledge is; c) what information or knowledge users’ feel they gained from participating in SPOCs; and d) whether users’ develop a commitment to the cause outside of social media.  

Future Work: The research presented here represents the first stage of a larger project. This stage aims to quantify whether social media users, who participated in SPOCs, participated or supported the campaign outside of social media. The next stage of this project will address related questions using a qualitative approach. The significance of our study lies in providing a better understanding of the effectiveness of these viral campaigns. Since many private and public companies, non-government organizations, health care initiatives and advocacy groups, to name a few, invest enormous amounts of time and resources into creating and disseminating these campaigns, we think that understanding how effective they are beyond their “15 minutes of fame” could help maximize the successfulness of the campaign’s goal(s) (and ultimately, effect change). 

Anderson, C. (2006). The long tail: Why the future of business is selling less of more. New York, NY: Hyperion. 
Corrigall-Brown, C. (2011). Patterns of protest: Trajectories of participation in social movements. Stanford, CA: Stanford University Press.  

Wednesday July 13, 2016 13:46 - 15:15
PSH (Professor Stuart Hall Building) - 326 Goldsmiths University, Building 2


Identifying the Influencers who Flooded Twitter during the #ALSicebucketchallenge
Location: PSH (Professor Stuart Hall Building) - 326, 
Goldsmiths, University of London, Building 2
Campus Map 

Contributor: Kelli Burns, University of South Florida, United States


Perhaps no other campaign has reached greater success in terms of participation, donations, social media chatter, and attention in popular culture than the ALS Ice Bucket Challenge. This campaign was truly a grassroots effort, fueled by friends of an ALS victim and then spread on social media by millions of participants ranging from average citizens to celebrities. Significant coverage by traditional media and participation by personalities on television further accelerated awareness of the campaign and the cause. On Twitter, many celebrities also shared their involvement in the campaign and spurred conversation and engagement among other Twitter users. 


This study sought to understand the impact of influencers in spreading discussion of the ALS Ice Bucket Challenge campaign on Twitter. This study examined the top accounts for retweets and mentions; the correlation between the most followed, most retweeted, and most mentioned users; and the network structure of the conversation. Finally, the conversation was examined to see whether it was "on message."


A historical data grant from Texifter provided access to almost 550,000 tweets and enterprise access to DiscoverText, a “cloud-based, collaborative text analytics solution.” Tweets were selected based on the criteria of inclusion of the hashtags of #alsicebucketchallenge or #icebucketchallenge and use of the English language during the period of August 18-22, 2014. Of the millions of English-language tweets during this timeframe using these hashtags, 15 percent of the tweets were randomly selected for inclusion in the sample, resulting in a sample size of 545,563 tweets. 


Analysis of the more than 500,000 tweets determined the top influencers in terms of mentions and retweets and also explored correlation between the most followed users, the most retweeted, and the most mentioned, finding a moderate correlation between the most retweeted and mentioned. T2G 0.3 for Python was used to extract all edges from tweets with multiple mentions from a sample of 1,000 tweets, resulting in 1,457 edges to be analyzed in NodeXL. Plotting the data using the Fruchterman-Reingold algorithm resulted in large nodes (representing the vertex in-degree metric) for certain celebrities and close relationships for others. The vertices were grouped by cluster using the Clauset-Newman-Moore algorithm and further examined. This study also modeled a larger sample of the dataset using Gephi and D3. Finally, although ALS was mentioned in a larger percentage of tweets, variations of the word donate were mentioned much less frequently.

Future Work: 

The implications of this study would be relevant to other activist Twitter campaigns that mobilize celebrity influencers. 

Wednesday July 13, 2016 13:46 - 15:15
PSH (Professor Stuart Hall Building) - 326 Goldsmiths University, Building 2


Social Resources Affecting Participation of Social Media Users in Poland
Location: PSH (Professor Stuart Hall Building) - 326, 
Goldsmiths, University of London, Building 2
Campus Map 

Contributor: Kamil Filipek, University of Warsaw, Poland


Nowadays, much of the public activities and behaviors can be found in social media. Social media became a tool enabling access, delivery, exchange and mobilization of resources embedded in personal networks. Whereas, the impact of such resources on instrumental and expressive actions is well documented in the literature (Lin 2001, Finsveen and van Oorschot 2008), the role of social media in facilitating/blocking different types of resources that may have na impact on the individual's actions remains little studied (Steinfield, Ellison, and Lampe 2008, Ellison et al. 2014). This research focus on the relationship between social media, individual social capital, and patterns of the political participation among Polish citizens.


Theory of social resources

The theory of social resources proposed by Lin (Lin, Vaughn, and Ensel 1981; Lin 1999; Lin 2001)⁠ makes explicit the assumption that resources embedded in personal networks have an impact on individual actions and can lead to better socioeconomic status (Lin 1999)⁠. He operationalized social capital at the individual level as 'a social asset by virtue of actors’ connections and access to resources in the network or group of which they are members' (Lin 2001). Such resources include symbolic and material goods that make up the social capital (Bourdieu 1986). To distinguish resources owned by others from private resources belonging to an individual, Lin introduced a term 'personal resources'. By personal resources he means “resources possessed by an individual [that] may include ownership of material as well as symbolic goods (e.g., diplomas and degrees)” (Lin 2001).

This research focus on social resources owned by individuals belonging to the respondent's personal network. Based on previous research with the Resource Generator tool (Webber, Huxley, & Harris 2011; Batorski, Bojanowski, & Filipek, 2015), it is assumed here that only some resources could be mobilized in a purposive action. In other words, relatives, friends and acquaintances may possess certain resources, but individuals are not able to use them when acting in various social contexts.

Thus, the main goal of this research is to find out whether and how resources embedded in personal networks (family, friends, acquaintances) influence the political participation of social media users. The following research questions are pursued:

- Do embedded and/or mobilizable resources in personal networks affect the political participiation of respondents?

- What is the impact of resources on respondents' activities selected in this research as indicators of the political participation?

- Whose resources, namely family, friends, acquaintances or respondents have an impact (positive or negative) on the political participation?



The core of the measuring tool is based on the Resource Generator (RG) (Van Der Gaag and Snijders 2005). Items included in the RG are the major independent variables. The RG items refer to the four types of resources (i.e. support, knowledge, recommendation, and material resources) embedded and mobilized through personal networks, that may have an impact on the individual's participation.

The dependent variable is represented by five items (5-point Likert scales) reflecting the respondents' political participation. Those items include (1) voting in elections, (2) signing petitions, (3) joining protests, (4) personal contacts with politicians, (5) local community meetings.

The data has been collected through the online questionnaire among individuals registered at the online research platform delivered by external partner. The research has been conducted in December 2015 on stratified random sample of 1000 (700 SM users and 300  non-users) residents of Poland.


The research shows that resources embedded in family, friends and acquaintances ties have an impact on the political participation of respondents. The impact of resources appears be either positive or negative depending on the activity selected for analysis. For example, resources that could be only accessed, but not mobilised by respondents have no impact on dependent variable defined as voting in elections. At the same time, resources that could be mobilized have positive impact on voting. When signing petition activity is examined the impact of resources is reversed. There is no effect of mobilizable resources and positive impact of resources that are embedded in individual's personal network. The strong ties (family and friends) are better source of embedded resources that have a positive impact on the political participation of social media users in Poland. In general, weak ties have no or negative effect on activities examined in this research. The only exception is voting in elections. It is found that resources mobilizable through weak ties may have a positive impact on respondents voting activity.

Thus, the amount and quality of social capital embedded in personal networks matter when the political participation is considered. Resources embedded in family, friends and acquaintances circles have an impact on certain activities exemplifying the political participation of social media users in Poland.

Future Work: 

The quantitative data will be combined with the qualitative data obtained via in-depth interviews based on position generator tool.


Batorski, D., Bojanowski, M., & Filipek, K. (2015). Getting a Job: Resources and Individual’s Chances on the Warsaw Labour Market. Polish Sociological Review, 192(4).

Bourdieu, P. (1986). The Forms of Capital. In J. G. Richardson (Ed.), Handbook of Theory of Research for the Sociology of Education (pp. 46–58). New York: Greenwood Press.

Ellison, N. B., Vitak, J., Gray, R., & Lampe, C. (2014). Cultivating Social Resources on Social Network Sites: Facebook Relationship Maintenance Behaviors and Their Role in Social Capital Processes. Journal of Computer-Mediated Communication, 19(4), 855–870. http://doi.org/10.1111/jcc4.12078

Finsveen, E., & van Oorschot, W. (2008). Access to Resources in Networks: A Theoretical and Empirical Critique of Networks as a Proxy for Social Capital. Acta Sociologica, 51(4), 293–307. http://doi.org/10.1177/0001699308097375

Lin, Nan. 1999. “Building a Network Theory of Social Capital” edited by N. Lin, K. S. Cook, and R. S. Burt. Connections 22(1):28–51.

Lin, Nan. 2001. Social Capital. A Theory of Social Structure and Action. Cambridge University Press.

Lin, Nan, John C. Vaughn, and Walter M. Ensel. 1981. “Social Resources and Occupational Status Attainment *.” Social Forces 59(4):1163–81. Retrieved (social resources, netoworks).

Steinfield, C., Ellison, N. B., & Lampe, C. (2008). Social capital, self-esteem, and use of online social network sites: A longitudinal analysis. Journal of Applied Developmental Psychology, 29(6), 434–445. http://doi.org/10.1016/j.appdev.2008.07.002

Van Der Gaag, M., & Snijders, T. a. B. (2005). The Resource Generator: social capital quantification with concrete items. Social Networks, 27(1), 1–29. http://doi.org/10.1016/j.socnet.2004.10.001

Webber, M., Huxley, P., & Harris, T. (2011). Social capital and the course of depression: Six-month prospective cohort study. Journal of Affective Disorders, 129(1-3), 149–157. http://doi.org/10.1016/j.jad.2010.08.005

Wednesday July 13, 2016 13:46 - 15:15
PSH (Professor Stuart Hall Building) - 326 Goldsmiths University, Building 2