Date of Original Version
Abstract or Description
We examine how online commenting is affected by different degrees of commenters’ identifiability: 1) real name accounts on social networking sites (or “real name-SNS accounts”; e.g., Facebook); 2) pseudonym accounts on social networking sites (or “pseudonym-SNS account”; e.g., Twitter); 3) pseudonymous accounts outside social networking sites (or “non-SNS accounts”; e.g., an online newspaper website’s account). We first construct a conceptual model of the relationship between the degree of identifiability and commenting behavior. When users can freely choose the account type between a non-SNS account and an SNS account to write a comment, the decision determines degree of identifiablity. This decision will be correlated to the propensity of using ‘offensive’ words (classified from a comprehensive list of swear terms in the Korean language) in their comments. To take endogeneity into consideration, we estimate a bivariate probit model between the choice of account type and the choice of using offensive words. We apply our model to a unique set of data consisting in over 75,000 comments attached to news stories collected from a variety of online news media websites. Our analysis highlights interesting dynamics between the degree of identifiability and commenters’ behavior. When commenters use an SNS account (which consists in a more identifiable condition) rather than a non-SNS account, they are less likely to use offensive words. We also find that the use of real name-SNS accounts (which provide an even more identifiable condition due to the disclosure of one’s real name), is associated with lower occurrence of offensive words than the case in which commenters use a pseudonym-SNS account for commenting. While the disclosure of true identity is likely to reduce the probability of using offensive words, the greater number of users seems to prefer participating in the commenting activity by using their pseudonym accounts
Proceedings of the Twelfth Workshop on the Economics of Information Security (WEIS 2013).