Yafei Wang

Linguistic alignment in text-based communication means that people tend to adjust their language use to one another both in terms of word choice and sentence structure. Previous studies about linguistic alignment suggested that these two forms of adaptation are correlated with each other, and that they build up to alignment at a higher representational level, such as pragmatic alignment for support functions. Two types of social support have been identified as important for online health… Show more

Linguistic alignment in text-based communication means that people tend to adjust their language use to one another both in terms of word choice and sentence structure. Previous studies about linguistic alignment suggested that these two forms of adaptation are correlated with each other, and that they build up to alignment at a higher representational level, such as pragmatic alignment for support functions. Two types of social support have been identified as important for online health communities (OHCs): emotional and informational support between support seekers and support providers. Do the two lower-level alignment measures (lexical and syntactic) relate to these two types of social support in the same way or, are they different? Our hypothesis was that they are similar, due to their correlation relationship. However, we found that, based on an analysis of a 10-year online forum for cancer survivors, the lower-level alignment measures have distinct relationships to the two higher-level support functions. In this paper, we describe this finding and its implications regarding potential refinement of the Interactive Alignment Model.
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Twitter has been recognized as a useful channel for the sharing and dissemination of health information, owing in part to its “retweet” function. This study reports findings from a content analysis of frequently retweeted obesity-related tweets to identify the prevalent beliefs and attitudes about obesity on Twitter, as well as key message features that prompt retweeting behavior conducive to maximizing the reach of health messages on Twitter. The findings show that tweets that are emotionally… Show more

Twitter has been recognized as a useful channel for the sharing and dissemination of health information, owing in part to its “retweet” function. This study reports findings from a content analysis of frequently retweeted obesity-related tweets to identify the prevalent beliefs and attitudes about obesity on Twitter, as well as key message features that prompt retweeting behavior conducive to maximizing the reach of health messages on Twitter. The findings show that tweets that are emotionally evocative, humorous, and concern individual-level causes for obesity were more frequently retweeted than their counterparts. Specifically, tweets that evoke amusement were retweeted most frequently, followed by tweets evoking contentment, surprise, and anger. In regard to humor, derogatory jokes were more frequently retweeted than nonderogatory ones, and in terms of specific types of humor, weight-related puns, repartee, and parody were shared frequently. Consistent with extant literature about obesity, the findings demonstrated the predominance of the individual-level (e.g., problematic diet, lack of exercise) over social-level causes for obesity (e.g., availability of cheap and unhealthy food). Implications for designing social-media-based health campaign messages are discussed. Show less

This paper quantifies linguistic alignment in long-term, online forum conversations; we demonstrate the existence of alignment at lexical and syntactic levels. Topicality- and priming-induced decay effects exist at a large scale in forum threads. However, adaptation to messages of specific social and interactional roles suggests that initial posts obtain a special role in the discourse.

Online cancer communities help members support one another, provide new perspectives about living with cancer, normalize experiences, and reduce isolation. The American Cancer Society's 166000-member Cancer Survivors Network (CSN) is the largest online peer support community for cancer patients, survivors, and caregivers. Sentiment analysis and topic modeling were applied to CSN breast and colorectal cancer discussion posts from 2005 to 2010 to examine how sentiment change of thread initiators,… Show more

Online cancer communities help members support one another, provide new perspectives about living with cancer, normalize experiences, and reduce isolation. The American Cancer Society's 166000-member Cancer Survivors Network (CSN) is the largest online peer support community for cancer patients, survivors, and caregivers. Sentiment analysis and topic modeling were applied to CSN breast and colorectal cancer discussion posts from 2005 to 2010 to examine how sentiment change of thread initiators, a measure of social support, varies by discussion topic. The support provided in CSN is highest for medical, lifestyle, and treatment issues. Threads related to 1) treatments and side effects, surgery, mastectomy and reconstruction, and decision making for breast cancer, 2) lung scans, and 3) treatment drugs in colon cancer initiate with high negative sentiment and produce high average sentiment change. Using text mining tools to assess sentiment, sentiment change, and thread topics provides new insights that community managers can use to facilitate member interactions and enhance support outcomes. Show less

The All-k-Nearest-Neighbor (AkNN) operation is common in many applications such as GIS and data analysis/mining. In this paper, for the first time, we study a novel variant of AkNN queries, namely All-Visible-k-Nearest-Neighbor (AVkNN) query, which takes into account the impact of obstacles on the visibility of objects. Given a data set P, a query set Q, and an obstacle set O, an AVkNN query retrieves for each point/object in Q its visible k nearest neighbors in P. We formalize the AVkNN query,… Show more

The All-k-Nearest-Neighbor (AkNN) operation is common in many applications such as GIS and data analysis/mining. In this paper, for the first time, we study a novel variant of AkNN queries, namely All-Visible-k-Nearest-Neighbor (AVkNN) query, which takes into account the impact of obstacles on the visibility of objects. Given a data set P, a query set Q, and an obstacle set O, an AVkNN query retrieves for each point/object in Q its visible k nearest neighbors in P. We formalize the AVkNN query, and then propose efficient algorithms for AVkNN retrieval, assuming that P, Q, and O are indexed by conventional data-partitioning indexes (e.g., R-trees). Our approaches employ pruning techniques and introduce a new pruning metric called VMDIST. Extensive experiments using both real and synthetic datasets demonstrate the effectiveness of our presented pruning techniques and the performance of our proposed algorithms. Show less

We study whether participants show alignment on social support in an online community. While adaptation can occur at linguistic and semantic levels, the relationships between alignment at multiple levels are neither theoretically nor empirically well understood. Our results indicate pragmatic alignment of forum participants along the axis of support type. We also find that lexical alignment is correlated with emotional support, and that the amount of lexical alignment is also correlated with… Show more

We study whether participants show alignment on social support in an online community. While adaptation can occur at linguistic and semantic levels, the relationships between alignment at multiple levels are neither theoretically nor empirically well understood. Our results indicate pragmatic alignment of forum participants along the axis of support type. We also find that lexical alignment is correlated with emotional support, and that the amount of lexical alignment is also correlated with the amount of pragmatic alignment. This finding can contribute to improving our understanding about the linguistic signature of different types of support, and enhancing theoretical model about the Interactive Alignment Model in a multi-party peer support conversation context. Show less

An increasing number of people use wearables and other smart devices to quantify various health conditions, ranging from sleep patterns, to body weight, to heart rates. Of these "Quantified Selfs", many choose to openly share their data via online social networks such as Twitter and Facebook. In this study, we use data for users who have chosen to connect their smart scales to Twitter, providing both a reliable time series of their body weight, as well as insights into their social… Show more

An increasing number of people use wearables and other smart devices to quantify various health conditions, ranging from sleep patterns, to body weight, to heart rates. Of these "Quantified Selfs", many choose to openly share their data via online social networks such as Twitter and Facebook. In this study, we use data for users who have chosen to connect their smart scales to Twitter, providing both a reliable time series of their body weight, as well as insights into their social surroundings and general online behavior. Concretely, we look at which social media features are predictive of physical sta- tus, such as body weight at the individual level, and activity patterns at the population level. We show that it is possible to predict an individual’s weight using their online social behaviors, such as their self-description and tweets. Weekly and monthly patterns of quantified-self behaviors are also discovered. These findings could contribute to building mod- els to monitor public health and to have more customized personal training interventions.

While there are many studies using either quantified self or social media data in isolation, this is one of the few that combines the two data sources and, to the best of our knowl- edge, the only one that uses public data. Show less