What Do People Like to "Share" About Obesity? A Content Analysis of Frequent Retweets About Obesity on Twitter.

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.

Temporal Causality Analysis of Sentiment Change in a Cancer Survivor Network.

Online health communities constitute a useful source of information and social support for patients. American Cancer Society's Cancer Survivor Network (CSN), a 173,000-member community, is the largest online network for cancer patients, survivors, and caregivers. A discussion thread in CSN is often initiated by a cancer survivor seeking support from other members of CSN. Discussion threads are multi-party conversations that often provide a source of social support e.g., by bringing about a change of sentiment from negative to positive on the part of the thread originator. While previous studies regarding cancer survivors have shown that members of an online health community derive benefits from their participation in such communities, causal accounts of the factors that contribute to the observed benefits have been lacking. We introduce a novel framework to examine the temporal causality of sentiment dynamics in the CSN. We construct a Probabilistic Computation Tree Logic representation and a corresponding probabilistic Kripke structure to represent and reason about the changes in sentiments of posts in a thread over time. We use a sentiment classifier trained using machine learning on a set of posts manually tagged with sentiment labels to classify posts as expressing either positive or negative sentiment. We analyze the probabilistic Kripke structure to identify the prima facie causes of sentiment change on the part of the thread originators in the CSN forum and their significance. We find that the sentiment of replies appears to causally influence the sentiment of the thread originator. Our experiments also show that the conclusions are robust with respect to the choice of the (i) classification threshold of the sentiment classifier; (ii) and the choice of the specific sentiment classifier used. We also extend the basic framework for temporal causality analysis to incorporate the uncertainty in the states of the probabilistic Kripke structure resulting from the use of an imperfect state transducer (in our case, the sentiment classifier). Our analysis of temporal causality of CSN sentiment dynamics offers new insights that the designers, managers and moderators of an online community such as CSN can utilize to facilitate and enhance the interactions so as to better meet the social support needs of the CSN participants. The proposed methodology for analysis of temporal causality has broad applicability in a variety of settings where the dynamics of the underlying system can be modeled in terms of state variables that change in response to internal or external inputs.

"Where Have All the Good Men Gone?" Gendered Interactions in Online Dating.

This article explores gendered patterns of online dating and their implications for heterosexual union formation. The authors hypothesized that traditional gender norms combine with preferences for more socially desirable partners to benefit men and disadvantage women in the earliest stages of dating. They tested this with 6 months of online dating data from a mid-sized southwestern city (N = 8,259 men and 6,274 women). They found that both men and women tend to send messages to the most socially desirable alters in the dating market, regardless of their own social desirability. They also found that women who initiate contacts connect with more desirable partners than those who wait to be contacted, but women are 4 times less likely to send messages than men. They concluded that socioeconomic similarities in longer term unions result, in part, from relationship termination (i.e., nonreciprocity) rather than initial preferences for similar partners.

Finding influential users of online health communities: a new metric based on sentiment influence.

OBJECTIVE: Online health communities (OHCs) have become a major source of support for people with health problems. This research tries to improve our understanding of social influence and to identify influential users in OHCs. The outcome can facilitate OHC management, improve community sustainability, and eventually benefit OHC users. METHODS: Through text mining and sentiment analysis of users' online interactions, the research revealed sentiment dynamics in threaded discussions. A novel metric--the number of influential responding replies--was proposed to directly measure a user's ability to affect the sentiment of others. RESULTS: Using the dataset from a popular OHC, the research demonstrated that the proposed metric is highly effective in identifying influential users. In addition, combining the metric with other traditional measures further improves the identification of influential users.

Understanding topics and sentiment in an online cancer survivor community.

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.

Modeling cognitive loads for evolving shared mental models in human-agent collaboration.

Recent research on human-centered teamwork highly demands the design of cognitive agents that can model and exploit human partners' cognitive load to enhance team performance. In this paper, we focus on teams composed of human-agent pairs and develop a system called Shared Mental Models for all--SMMall. SMMall implements a hidden Markov model (HMM)-based cognitive load model for an agent to predict its human partner's instantaneous cognitive load status. It also implements a user interface (UI) concept called shared belief map, which offers a synergic representation of team members' information space and allows them to share beliefs. An experiment was conducted to evaluate the HMM-based load models. The results indicate that the HMM-based load models are effective in helping team members develop a shared mental model (SMM), and the benefit of load-based information sharing becomes more significant as communication capacity increases. It also suggests that multiparty communication plays an important role in forming/evolving team SMMs, and when a group of agents can be partitioned into subteams, splitting messages by their load status can be more effective for developing subteam SMMs.

SRCAST-Diagnosis: understanding how different members of a patient-care team interact with clinical decision support system.

A clinical diagnosis is a decision-making process that consists of not only the final diagnostic decision but also a series of information seeking decisions. Members of a patient-care team such as nurses, residents, and attending physicians play different roles but work collaboratively during this process. To better support the different roles and their collaborations during this process, we need to understand how different users interact with decision support systems. We developed SRCAST-Diagnosis to test how nurses, residents, and attending physicians use decision support system to improve diagnosis accuracy and resource efficiency. Nurses seemed more willing to take recommendations and therefore saved a greater amount of lab resources, but made less improvements on diagnosis accuracy. Attending physicians appeared more cautious in accepting SRCAST-Diagnosis recommendations. These findings will provide useful information for future CDSS design to support better collaborations of team members.

A distributed, collaborative intelligent agent system approach for proactive postmarketing drug safety surveillance.

Discovering unknown adverse drug reactions (ADRs) in postmarketing surveillance as early as possible is of great importance. The current approach to postmarketing surveillance primarily relies on spontaneous reporting. It is a passive surveillance system and limited by gross underreporting (<10% reporting rate), latency, and inconsistent reporting. We propose a novel team-based intelligent agent software system approach for proactively monitoring and detecting potential ADRs of interest using electronic patient records. We designed such a system and named it ADRMonitor. The intelligent agents, operating on computers located in different places, are capable of continuously and autonomously collaborating with each other and assisting the human users (e.g., the food and drug administration (FDA), drug safety professionals, and physicians). The agents should enhance current systems and accelerate early ADR identification. To evaluate the performance of the ADRMonitor with respect to the current spontaneous reporting approach, we conducted simulation experiments on identification of ADR signal pairs (i.e., potential links between drugs and apparent adverse reactions) under various conditions. The experiments involved over 275,000 simulated patients created on the basis of more than 1000 real patients treated by the drug cisapride that was on the market for seven years until its withdrawal by the FDA in 2000 due to serious ADRs. Healthcare professionals utilizing the spontaneous reporting approach and the ADRMonitor were separately simulated by decision-making models derived from a general cognitive decision model called fuzzy recognition-primed decision (RPD) model that we recently developed. The quantitative simulation results show that 1) the number of true ADR signal pairs detected by the ADRMonitor is 6.6 times higher than that by the spontaneous reporting strategy; 2) the ADR detection rate of the ADRMonitor agents with even moderate decision-making skills is five times higher than that of spontaneous reporting; and 3) as the number of patient cases increases, ADRs could be detected significantly earlier by the ADRMonitor.

Hypothesis-driven story building framework: enhancing iterative process support in clinical diagnostic decision support systems.

Clinical diagnosis is an iterative process because of partial and ambiguous information, changing conditions, and resource constraints. Although clinical diagnostic decision support systems have been successfully used to support clinical care, they face certain limitations in supporting clinical diagnosis as an iterative process. An approach is required to enhance the iterative process support in clinical diagnostic decision support systems. We model the clinical diagnosis process as a hypothesis-driven story building, and implement a prototype clinical diagnostic decision support system that is able to generate and evaluate differential diagnoses, narrow and revise the diagnoses based on newly obtained information, and prioritize resources for information seeking.

Challenges to effective crisis management: using information and communication technologies to coordinate emergency medical services and emergency department teams.

OBJECTIVE: The purpose of this study is to identify the major challenges to coordination between emergency department (ED) teams and emergency medical services (EMS) teams. DESIGN: We conducted a series of focus groups involving both ED and EMS team members using a crisis scenario as the basis of the focus group discussion. We also collected organizational workflow data. RESULTS: We identified three major challenges to coordination between ED and EMS teams including ineffectiveness of current information and communication technologies, lack of common ground, and breakdowns in information flow. DISCUSSION: The three challenges highlight the importance of designing systems from socio-technical perspective. In particular, these inter-team coordination systems must support socio-technical issues such as awareness, context, and workflow between the two teams.

A team agent approach to postmarketing surveillance of adverse drug reactions.

Current postmarketing surveillance methods largely rely on spontaneous reports which suffer from serious underreporting, latency, and inconsistent reporting. Thus they are not ideal for rapidly identifying rare adverse drug reactions (ADRs). We propose an active, multi-agent computer software system, where each agent is empowered with teamwork capabilities such as anticipating information needs, identifying relevant ADR information, and continuously monitoring and proactively sharing such information in a collaborative fashion with other agents. The main purpose of this system is to help regulatory authorities (e.g., FDA in the U.S.) find previously unrecognized ADRs as early as possible. Another objective is to promote increased filing of on-line ADR reports thereby, addressing the severe underreporting problem with the current system. The proposed system has the potential to significantly accelerate the process of ADR discovery and response by utilizing electronic patient data distributed across many different sources and locations more effectively. Our preliminary system design is presented and some issues related to it are discussed.