Modeling Global and Local Interactions for Online Conversation Recommendation

The popularity of social media platforms results in a huge volume of online conversations produced every day. To help users better engage in online conversations, this article presents a novel framework to automatically recommend conversations to users based on what they said and how they behaved in their chatting histories. While prior work mostly focuses on post-level recommendation, we aim to explore conversation context and model the interaction patterns therein. Furthermore, to characterize personal interests from interleaving user interactions, we learn (1) global interactions , represented by topic and discourse word clusters to reflect users’ content and pragmatic preferences, and (2) local interactions , encoding replying relations and chronological order of conversation turns to characterize users’ prior behavior. Built on collaborative filtering, our model captures global interactions via discovering word distributions to represent users’ topical interests and discourse behaviors, while local interactions are explored with graph-structured networks exploiting both reply structure and temporal features. Extensive experiments on three datasets from Twitter and Reddit show that our model coupling global and local interactions significantly outperforms the state-of-the-art model. Further analyses show that our model is able to capture meaningful features from global and local interactions, which results in its superior performance in conversation recommendation.

Observation Effect in Ecological Momentary Assessments: A Study of Sun Protection Practices

Daily diaries and ecological momentary assessments are plagued by the assessment itself becoming an intervention, known as the observation effect. Bayesian hierarchical level modeling is a technique to analyze repeated measures or multiple outcomes. In a study of twice-daily self-reporting of sun protection behavior among high-risk individuals, we investigate observation effects, agreement between retrospectively self-reported reminder effect and observation effect, differential observation effects, and consistency of behaviors. Participants who retrospectively reported no reminder effect showed a decrease in protective behaviors over time, whereas those who reported they were reminded showed sustained use. Advantages of the Bayesian methodology are demonstrated for assessing consistency of behaviors. Although we cannot observe prior behavior, we theorize that individuals experience an initial elevation at the onset of observation, though this unobserved increase is only sustained for a subset who later attribute this sustained behavior to a reminder effect. Implications for study designs with repeated observations are discussed.

The effects of exposure to predators on personality and plasticity

Past experiences are known to affect average behavior but effects on “animal personality”, and plasticity are less well studied. To determine whether experience with predators influences these aspects, we compared the behavior of Gryllodes sigillatus before and after exposure to live predators. We found that emergence from shelter and distance moved during open-field trials (activity) changed after exposure, with individuals becoming less likely to emerge from shelters but more active when deprived of shelter. We also found that plasticity in activity increased after exposure to predators and some indications that differences among individuals (i.e. “personality”) in emergence from shelter and the amount of an arena investigated increased after exposure. Our results demonstrate that experience with predators affects not only the average behavior of individuals but also how individuals differ from each other—and their own prior behavior—even when all individuals have the same experiences.

Network models of driver behavior

The way people behave in traffic is not always optimal from the road safety perspective: drivers exceed speed limits, misjudge speeds or distances, tailgate other road users or fail to perceive them. Such behaviors are commonly investigated using self-report-based latent variable models, and conceptualized as reflections of violation- and error-proneness. However, attributing dangerous behavior to stable properties of individuals may not be the optimal way of improving traffic safety, whereas investigating direct relationships between traffic behaviors offers a fruitful way forward. Network models of driver behavior and background factors influencing behavior were constructed using a large UK sample of novice drivers. The models show how individual violations, such as speeding, are related to and may contribute to individual errors such as tailgating and braking to avoid an accident. In addition, a network model of the background factors and driver behaviors was constructed. Finally, a model predicting crashes based on prior behavior was built and tested in separate datasets. This contribution helps to bridge a gap between experimental/theoretical studies and self-report-based studies in traffic research: the former have recognized the importance of focusing on relationships between individual driver behaviors, while network analysis offers a way to do so for self-report studies.

Consumer-Level Determinants of Wine Purchases in Canadian Restaurants

Logistic regressions identified determinants of red and white wine purchases in formal and casual restaurants, using a detailed dataset of over 26,000 consumer-level food away-from-home purchases in two Canadian provinces during 2000–2005. Meal context regressors, and prior behavior associated mainly with unobserved heterogeneity, contributed most of the explanatory power, with observable demographic regressors playing a modest role. The main strategic recommendation is thus to focus wine marketing resources on the restaurant environment, with less emphasis on targeting specific audiences. (JEL Classification: C25, D12)