#Influence12 – Group Discussions (Sat. Sep 29)

		Lead Discussant	Participants
Group 1	Qualitative Approaches	Diane Rasmussen Neal (University of Western Ontario, Canada)	DeNel Rehberg Sedo, MSVU Sanjay Khanna, CGI Cornelius Puschmann, Humboldt-Universität zu Berlin Mary Cavanagh, University of Ottawa Kirstie Hawkey, Dalhousie University Nadine Desrochers, Université de Montréal Caroline Whippey, University of Western Ontario Dipak Gupta, San Diego State University, Ruth Collings, Dalhousie University Lou Duggan, Saint Mary’s University Jodi Asbell-Clarke, TERC
	Discussion Questions: What research questions about social media and influence can we answer with qualitative data collection and analysis methods? How do we attempt to choose the “correct” qualitative methods for studying social media — methods that will give us authentic and meaningful answers? Since quantitative approaches are viewed as the standard for measuring influence, how can we demonstrate the complementary importance of qualitative methods in this area of research?
Group 2	Social Network Analysis	Bernie Hogan (University of Oxford, UK)	James Boxall, Dalhousie GISciences Centre Sam Stewart, Dalhousie University Md. Maidul, Islam Eastern University Jonathan Mulle, Dalhousie University Jiue-An (Jay) Yang, San Diego State University Anatoliy Gruzd, Dalhousie University Esther Brainin, Ruppin Academic Center, Israel Naureen Nizam, Dalhousie University Anabel Quan-Haase, University of Western Ontario Regina Collins, New Jersey Institute of Technology Elena Grewal, Stanford University
	Discussion Questions: 1. To what extent does influence in one social media platform translate into influence on another. That is, do my recommendations, tastes and advice have as much sway on Twitter as on linked in? 2. Do differences in platform design make a difference to network structure and if so how? 3. When is it useful to think about configurations or motifs for influence? That is, when do we think not just about centrality/position and not about community structure but the triads and cycles that lead to larger network structures. 4. Will there ever be a network that consolidates all the different social network sites and if so how? If not, what are the barriers both practical and technical?
Group 3	Political Mobilization & Engagement	Greg Elmer (Toronto Metropolitan University, Canada)	Crystal Sutherland, Nova Scotia Advisory Council on the Status of Women Christopher Helland, Dalhousie University Cynthia Alexander, Acadia University Brittany White, Dalhousie University Philip Mai, Dalhousie University Yimin Chen, University of Western Ontario Jake Wallis, Charles Sturt University John Piorkowski, UMBC Andrea Lauder, University of Alberta
	Discussion Questions: Is the term “campaign” still relevant for social media enacted forms of political communication? What are its limitations? What do we mean when we suggest that social media “effect” the political process/sphere? Do we need a need “effects” paradigm for networked politics? How can we assure scientific/scholarly rigour in our studies of social media when platforms consistently obscure — and prohibit access to – data needed for the analysis of online political communication?
Group 4	Social Media and Marketing	Ramesh Venkat (Saint Mary’s University, Canada)	Madeline Driscoll, Dalhousie University Lara Killian, commandN.tv David Neilsen, Mount Saint Vincent University Adrian J. Ebsary, University of Ottawa Gabriella Mosquera, Dalhousie University Chang Zhe Lin, University of Toronto Catherine Dumas, SUNY Albany Jenna Jacobson, University of Toronto Les Servi, MITRE Sharon Rundle, Mount Saint Vincent University Christine Larade, Social Daisy
	Discussion Questions: How do we reliably assess the impact of social influence occurring via social media on buyer preferences? How can opinion leaders in social media be identified by marketers? How can we define brand engagement in the context of social media? What would a brand engagement metric include? (“likes” and “retweets” don’t seem be strongly connected to buyer behavior).
Group 5	Opinion Mining and Sentiment Analysis	Nancy McCracken (Syracuse University, USA)	Jean Gawron, San Diego State University Masoud Makrehchi, University of Ontario Institute of Technology David Arllen, MITRE Kashif Raza, Dalhousie University Alexander Lent, McGill University Gobaan Raveendran, University of Waterloo Jasy Liew Suet Yan, Syracuse University Nouf Khashman, McGill University Aleksandr Semenov, NRU HSE Kim Martin, University of Western Ontario
	Our overall research question is: How do we use opinion mining and sentiment analysis for studying influence on social media? As a result of our discussion, we would like to set up some future directions for this research area. What are the important aspects we can explore and contribute to the current body of knowledge? To help answer this, we suggest the following discussion questions. Discussion Questions 1. How is opinion mining or sentiment analysis related to studying influence on social media, in other words, what is the motivation of studying influence using opinion mining and sentiment analysis? Are there any theories of how one’s opinions or sentiment, or how they express them, affects their influence on social media? 2. What specific questions of opinion mining or sentiment analysis and in what types of social media would be useful? Within the broad outlines of detecting sentiment or opinion, researchers often investigate more specific questions, such as recognizing stance or disagreement in online debates or detecting sarcasm on Twitter. Which ones of these do you think are important for determining influence (you may want to think of specific influence scenarios) or what other ones do you know of or would you propose? 3. Current methods for sentiment analysis and opinion mining usually fall into the unsupervised camp, with methods such as counting the number of positive or negative words in a sentiment lexicon, or into the supervised camp, with machine learning methods that required data annotated either by humans or taken from situations where the results are known. Can we apply these existing opinion mining/sentiment analysis methods for the study of influence on social media? What are the available tools? How do we use these tools for this specific purpose? What are the challenges? 4. Accuracy of opinion mining and sentiment analysis generally ranges in the 70-80% in the most successful cases, where accuracy is defined by comparing each document or utterance. (And in many cases, human inter-annotator agreement is about 80% at best.) Is this type of accuracy good enough for use in influence applications? Are there other types of evaluations that would be useful?