A Tale of Two Cultures: Comparing Interpersonal Information Disclosure Norms on Twitter

We present an exploration of cultural norms surrounding online disclosure of information about one's interpersonal relationships (such as information about family members, colleagues, friends, or lovers). Our study extends the prior literature on Multi-Party Privacy and information disclosure by probing on cultural differences. In order to identify tweets about one's interpersonal relationships, we performed a two step process. First, we utilized a card-sort study to develop a culturally-sensitive saturated taxonomy of words that represent interpersonal relationships (e.g., ma, mom, mother). Then we developed a high-accuracy interpersonal disclosure detector based on dependency-parsing (F1-score: 86\%) to identify when the words refer to a personal relationship of the poster (e.g., "my mom" as opposed to "a mom"). This allowed us to filter through more than 2 million tweets posted in the U.S. and India over a 3 month period. We thus identified 400K+ tweets that disclosed information about the poster's interpersonal relationships. Prior literature identifies the cultural dimension of individualism versus collectivism as being a major determinant of offline communication differences in terms of emotion, topic, and content disclosed. Thus, we took a mixed methods approach to analyze these differences between tweets from an individualist (U.S.) versus collectivist (India) society (e.g., comparing the amount of joy expressed about one's family). We identify several cultural differences in disclosure behaviors. We also reveal how a combination of qualitative and quantitative methods are needed to uncover these differences; Using just one or the other can be misleading. The paper concludes by providing recommendations on how future systems designers can study and design for culturally-sensitive interpersonal disclosure norms.