ONSI: Oregon Networked Society Initiative
Major Online Social Networks represent online societies that are of growing interest among different research communities ranging from Computer Science to Sociology, Psychology, Linguistics and Political Science. However, these communities rely on completely different approaches for data collection and data analysis. Social sciences often rely on small-scale data collection through customized questionnaire that primarily focus on complex user attributes such as personality traits and require human-in-the-loop analysis to answer a very specific (narrow scooped) questions. Alternatively, Computer scientists obtain large-scale datasets through active measurements and conduct data driven analysis that reveals major trends in various user or network characteristics. However, these findings are often general and it is difficult to determine the root causes (e.g. user personality or OSN services) of any discovered characteristics.
The goal of interdisciplinary this project is to bridge the gap in the methodologies of Computer and Social scientists. Toward this end, this project brings together a collection of researchers from different department across UO campus (including CIS, Psychology, Sociology, Business, Linguistic, Political Science, Communications and Economics) to explore a wide range of interdisciplinary research question in the context of one or more major OSNs such as Facebook, Twitter and Google+.
ProjectsPIRO: Personality, Identity, and Reputation Online (led by Sanjay Sirvastava)
In face-to-face social networks, reputations can be established very quickly and based on very little information. Is the same true online? We are studying how people form impressions of one another based on different kinds of information that are publicly available through social network services. What are the important kinds of impressions that people form? When different people looking at the same information about the same user, how much do their impressions agree or disagree? Do different kinds of information convey different impressions? To answer these questions, we are currently looking at the information contained in Twitter users' profiles and how it conveys impressions about the user's personality and social standing. We are also looking at whether people form impressions of a target Twitter user based on who is in that user's social network -- who the user follows, and who follows the user.
Sports Marketing: Innovation and Collaboration Through Communicating Lifestyle (led by Lynn Kahle)
Effective sports marketing must convey lifestyle information to fans. Monday Night Football seeks to attract young American males with an interest in sports to a prime-time slot, for example, to promote products associated with the sport lifestyle (e.g., tough, rugged vehicles or macho beverages). The marketer has only some control over events and messages, and the manager must work with each day’s developments. Fans have long memories and deep opinions. Social media have changed how marketing communications works. Social media share characteristics such as instantaneous communication, interactivity, potentially widespread distribution (but conversely the potential to be highly targeted), democracy of content (often without editors), experimental format, low cost, and alterability. Social media group members have self-selected into a category that embraces a cluster of lifestyle motives. Those social media groups may not always have an immediately obvious theme, but the groups are not statistical abstractions. Real people have expended a real effort to associate with certain other real people for some purpose. Because of these aggregations, the importance of understanding lifestyle is today infinitely more valuable and more important for effective marketing than it was in previous generations. Now social media provide opportunities for aggressive interaction with customers in lifestyle groups, for purposes ranging from detecting complaints to brand extensions to product co-creation. This reseaerch presents an analysis of Twitter messages to illustrate how sports-related communication reaches consumers and what the implications are for best practices of marketing managers, testing hypotheses derived from the social adaptation theory version of functional theory. We look at 4 baseball teams (Red Sox, Yankees, Dodgers, Giants) and 4 sports apparel companies (Nike, Adidas, Reebok, and Under Armour) to count the number and type of value references and the number of pronouns. The data help us understand how to collaborate with consumers to direct their attention to the lifestyle message of the brands