Krzysztof Sobieszek, University of Warsaw Dominik Batorski, University of Warsaw Łukasz Bolikowski, University of Warsaw Opinion leaders in social media (mixed-mode approach) Contact: General Online Research Conference GOR March 2014, Cologne University of Applied Sciences, Germany

Agenda Theoretical background on opinion leadership Available measures Research questions Mix-mode study model Data description (step 1) Sample results (step 1) Design for study continuation (step 2) Conclusions

Opinion leadership Concept derived from media research. Katz and Lazersfeld (1955) first indicates on two-step flow of media influence Individuals influenced more by exposure to each other than the media Small minority of opinion leaders intermediaries between mass-media and majority of society. Media responsible mainly for agenda setting process Opinion leaders are responsible for process of opinion formation on issues form agenda formed by from media.

Influential hypothesis Very popular and broadly used concept in social sciences In 60s reported as one of the most important formulations in the behavioral sciences (Arndt, 1967) In 70s – dominant paradigm of media sociology (Gitlin, 1978) Over studies of influential, opinion leaders personal influence (Weimann, 1994) „…guiding theme for diffusion and marketing research.” (Burt, 1999) Original Katz & Lazarsfeld definition: „individuals who were like to influence other persons in their immediate environment”. It is used more or less in it’s previous form across literature (Greval, Mehta, Kardes, 2000) Lately it is adopted by many large–scale networks studies.

Discussion over „influential hypothesis” Watts, Dodds (2007) stated that: Although the two-step flow model remains very popular and broadly used Mechanism of influence itself remains unclear It is left unspecified or asserted to derive from some diffusion or influence process/theory, i.e. Diffusion of innovation theory (Rogers, 1995) Different social contagion models (Aral, Walker, 2012) Study of Watts, Dodds (2007) testing different influence models reveals that in many cases role of influentials is overestimated Need for further research based on several assumptions over nature of personal influence, structure of influence networks, etc. to test the Influential hypothesis with it’s more sophisticated nature

Broad context of influence study Research on opinion leadership as it was mentioned remains central and consider very useful in many fields from media research through marketing to health policy administration (i.e. obesity epidemic) All this fields are covered by 3 fundamentally different processes: Personal influence on opinions Diffusion of innovation (i.e. new products) [Coleman, Katz, Menzel (1966), Rogers (1995), Valente (1995)] Diffusion of information within networks This outlines even broader context for the study of opinion leaders / influencers Many theories might not directly indicates two-step flow model but provide useful measures on individuals differentiation considering influence on others and role in diffusion process

Measures of opinion leadership (1) Original conceptualization (Katz, 1957): expression of values (who one is) professional competence (what one knows) nature of social network (whom one knows) Combination of personal and social factors

Research question How different variables predicts opinion leadership (influence) Behavioral/processual measures (dependent variable) Actual influence or position in diffusion process modeled from actual network data or experimental approach Actual behaviors (Sedikides, Jackson, 1990).

Predictors of influence 1. Personal characteristic of individuals: personality measures, demographic, socioeconomic, etc. 2. Declarative Self perceived/ self reported Sociometric or sample-sociometric – (Valente, 2007) 3. Position of influentials (peers) within social structure (or general social structure descriptions) i.e. centrality measures (Freeman, 1978, 1979, 1980)

Mixed-mode study model 1st Study Large-scale network data (NK.pl) Statistical analysis Social network analysis Differentiate users in terms o level of influence Finding correlates of influence 2nd Study Declarative data (NK.pl) Sample-sociometric approach Comparable data on perceived influence level (myself and others) Focusing on information diffusion/flow

NK.pl Polish social networking site established in 2006 as nasza-klasa.pl Reunion idea (classmates.com) In 2010 rebranding for NK.pl, evolution to full communication platform (wide spectrum of social features, games, virtual currency, messenger, etc.) In : 14m active monthly users, NK.pl generates 25% of polish internet traffic Decreasing role on polish internet market, currently 7m active users (1,5m daily)

Data sets from NK.pl Characteristics of users: gender, age, city, logins, photos added,, comment on photos, comments on profile, number of friends, number of stars received, etc. Table of all links published by users with stars and comments Table of users giving comments and stars on other users’ published links Relation (friendship) structure of network 12 weeks from October to December 2013 All users data available but only active users are analyzed (min 1 login within analyzed period)

Minority of users is sharing links

Skewed distributions among sharing users

Skewed distributions of shared links

Predictors of influence – early results Influencers = top 5% of distribution of reaction from others (stars and comments (mean = 5 reactions) Logistic Regression Model (Nagelkerk’s R square = 0,336)

Older users are more likely to receive more comments and stars to their shared links Possible explanations: older people are sharing content that is more engaging Specific for NK.pl – younger users leaving to FB and using NK mostly for games

Influencers give stars and comments to significantly larger number of other users Reciprocity is one of important mechanisms building influence

2nd Study design Sample-sociometric approach Survey on n~3000 sample on NK.pl (invitations by native massage system) 1.Each respondent evaluates general influence he has on others 2.For each respondent we display 4 of his friends 4 friends displayed differs in terms of comments and shares received They are evaluated by respondent in terms of influence Model will allow to join data form 1st and 2nd study. It will allow to evaluate if declarative/sociometric measures are good predictors of influence