We will be hearing two CSCW practice talks, from Ge Gao and Amit Sharma. CSCW (Computer-Supported Cooperative Work and Social Computing) is an ACM conference for research in the design and use of technologies that affect groups, organizations, communities, and networks.
Ge's paper is titled Two is Better Than One: Improving Multilingual Collaboration by Giving Two Machine Translation Outputs. Here's the abstract:
Machine translation (MT) creates both opportunities and challenges for multilingual collaboration: While MT enables collaborators to communicate via their native languages, it can introduce errors that make communication difficult. In the current paper, we examine whether displaying two alternative translations for each message will improve conversational grounding and task performance. We conducted a laboratory experiment in which monolingual native English speakers collaborated with bilingual native Mandarin speakers on a map navigation task. Each dyad performed the task in one of three communication conditions: MT with single output, MT with two outputs, and English as a common language. Dyads given two translations for each message communicated more efficiently, and performed better on the task, than dyads given one translation. Our findings show the value of providing multiple translations in multilingual collaboration, and suggest design features of future MT-based collaboration tools.
Amit's paper is titled Studying and Modeling the Connection between People's Preferences and Content Sharing. Here's the abstract:
People regularly share items using online social media. However, people's decisions around sharing---who shares what to whom and why---are not well understood. We present a user study involving 87 pairs of Facebook users to understand how people make their sharing decisions. We find that even when sharing to a specific individual, people's own preference for an item (individuation) dominates over the recipient's preferences (altruism). People's open-ended responses about how they share, however, indicate that they do try to personalize shares based on the recipient. To explain these contrasting results, we propose a novel process model of sharing that takes into account people's preferences and the salience of an item. We also present encouraging results for a sharing prediction model that incorporates both the senders' and the recipients' preferences. These results suggest improvements to both algorithms that support sharing in social media and to information diffusion models.
As usual, there will be snacks from Manndible - we look forward to having you join us!