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Background: Although many people with serious diseases participate in online support communities, little research has investigated how participants elicit and provide social support on these sites. Objective: The first goal was to propose and test a model of the dynamic process through which participants in online support communities elicit and provide emotional and informational support. The second was to demonstrate the value of computer coding of conversational data using machine learning techniques (1) by replicating results derived from human-coded data about how people elicit support and (2) by answering questions that are intractable with small samples of human-coded data, namely how exposure to different types of social support predicts continued participation in online support communities. The third was to provide a detailed description of these machine learning techniques to enable other researchers to perform large-scale data analysis in these communities. Methods: Communication among approximately 90,000 registered users of an online cancer support community was analyzed. The corpus comprised 1,562,459 messages organized into 68,158 discussion threads. Amazon Mechanical Turk workers coded (1) 1000 thread-starting messages on 5 attributes (positive and negative emotional self-disclosure, positive and negative informational self-disclosure, questions) and (2) 1000 replies on emotional and informational support. Their judgments were used to train machine learning models that automatically estimated the amount of these 7 attributes in the messages. Across attributes, the average Pearson correlation between human-based judgments and computer-based judgments was .65. Results: Part 1 used human-coded data to investigate relationships between (1) 4 kinds of self-disclosure and question asking in thread-starting posts and (2) the amount of emotional and informational support in the first reply. Self-disclosure about negative emotions (beta=.24, P <.001), negative events (beta=.25, P <.001), and positive events (beta=.10, P =.02) increased emotional support. However, asking questions depressed emotional support (beta=–.21, P <.001). In contrast, asking questions increased informational support (beta=.38, P <.001), whereas positive informational self-disclosure depressed it (beta=–.09, P =.003). Self-disclosure led to the perception of emotional needs, which elicited emotional support, whereas asking questions led to the perception of informational needs, which elicited informational support. Part 2 used machine-coded data to replicate these results. Part 3 analyzed the machine-coded data and showed that exposure to more emotional support predicted staying in the group longer 33% (hazard ratio=0.67, P <.001), whereas exposure to more informational support predicted leaving the group sooner (hazard ratio=1.05, P <.001). Conclusions: Self-disclosure is effective in eliciting emotional support, whereas question asking is effective in eliciting informational support. Moreover, perceptions that people desire particular kinds of support influence the support they receive. Finally, the type of support people receive affects the likelihood of their staying in or leaving the group. These results demonstrate the utility of machine learning methods for investigating the dynamics of social support exchange in online support communities. [J Med Internet Res 2015;17(4):e99]

We investigate the role of networks of alliances in preventing (multilateral) interstate wars. We first show that, in the absence of international trade, no network of alliances is peaceful and stable. We then show that international trade induces peaceful and stable networks: trade increases the density of alliances so that countries are less vulnerable to attack and also reduces countries' incentives to attack an ally. We present historical data on wars and trade, noting that the dramatic drop in interstate wars since 1950, and accompanying densification and stabilization of alliances, are consistent with the model but not other prominent theories. Comment: 39 pages, 14 figures

This paper discusses (a) the role of cognitive and noncognitive ability in shaping adult outcomes, (b) the early emergence of differentials in abilities between children of advantaged families and children of disadvantaged families, (c) the role of families in creating these abilities, (d) adverse trends in American families, and (e) the effectiveness of early interventions in offsetting these trends. Practical issues in the design and implementation of early childhood programs are discussed. Paper presented as the Presidential Lecture of the Western Economics Association, Seattle Washington, June 30, 2007 National Institutes of Health (NIH) JB & MK Pritzker Family Foundation Committee for Economic Development Susan Thompson Buffet Foundation National Science Foundation American Bar Foundation