People on Drugs: Credibility of User Statements in Health Communities

Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information

Peer coaching through mHealth targeting physical activity in people with Parkinson disease: feasibility study

BACKGROUND: Long-term engagement in exercise and physical activity mitigates the progression of disability and increases quality of life in people with Parkinson disease (PD). Despite this, the vast majority of individuals with PD are sedentary. There is a critical need for a feasible, safe, acceptable, and effective method to assist those with PD to engage in active lifestyles. Peer coaching through mobile health (mHealth) may be a viable approach. OBJECTIVE: The purpose of this study was to develop a PD-specific peer coach training program and a remote peer-mentored walking program using mHealth technology with the goal of increasing physical activity in persons with PD. We set out to examine the feasibility, safety, and acceptability of the programs along with preliminary evidence of individual-level changes in walking activity, self-efficacy, and disability in the peer mentees. METHODS: A peer coach training program and a remote peer-mentored walking program using mHealth was developed and tested in 10 individuals with PD. We matched physically active persons with PD (peer coaches) with sedentary persons with PD (peer mentees), resulting in 5 dyads. Using both Web-based and in-person delivery methods, we trained the peer coaches in basic knowledge of PD, exercise, active listening, and motivational interviewing. Peer coaches and mentees wore FitBit Zip activity trackers and participated in daily walking over 8 weeks. Peer dyads interacted daily via the FitBit friends mobile app and weekly via telephone calls. Feasibility was determined by examining recruitment, participation, and retention rates. Safety was assessed by monitoring adverse events during the study period. Acceptability was assessed via satisfaction surveys. Individual-level changes in physical activity were examined relative to clinically important differences. RESULTS: Four out of the 5 peer pairs used the FitBit activity tracker and friends function without difficulty. A total of 4 of the 5 pairs completed the 8 weekly phone conversations. There were no adverse events over the course of the study. All peer coaches were "satisfied" or "very satisfied" with the training program, and all participants were "satisfied" or "very satisfied" with the peer-mentored walking program. All participants would recommend this program to others with PD. Increases in average steps per day exceeding the clinically important difference occurred in 4 out of the 5 mentees. CONCLUSIONS: Remote peer coaching using mHealth is feasible, safe, and acceptable for persons with PD. Peer coaching using mHealth technology may be a viable method to increase physical activity in individuals with PD. Larger controlled trials are necessary to examine the effectiveness of this approach.This study is supported by Boston Roybal Center for Active Lifestyle Interventions (RALI Boston), Grant #P30 AG048785, and the American Parkinson Disease Association, Massachusetts chapter. The authors would like to thank Nicole Sullivan, SOT, for her assistance with data management and data collection and Nick Wendel, DPT, for his assistance with data collection. Additionally, the authors would like to thank the participants in this study for their time, effort, and insights. (P30 AG048785 - Boston Roybal Center for Active Lifestyle Interventions (RALI Boston); American Parkinson Disease Association, Massachusetts chapter)Accepted manuscrip

Citizen-led Work using Social Computing and Procedural Guidance

Online platforms enable people to interact with friends, family, and the world at large. How might people go beyond sharing stories and ideas to building and testing theories in the real world? While many are motivated to dig deeper into their lived experience, limited expertise and lack of platform support make complex activities like experimentation dauntingly hard. Novices benefit greatly from expert guidance: this thesis advocates baking the guidance into the interface itself.This dissertation introduces procedural guidance to build just-in-time expertise for difficult tasks. Procedural guidance has multiple advantages: it is minimal, leverages teachable moments, and can be ability-specific. This dissertation instantiates this insight of procedural guidance through a sequence of increasingly complex social computing systems: Gut Instinct for curating ideas, Docent for generating hypotheses, and Galileo for citizen-led experiments.Gut Instinct hosts online learning materials and enables people to collaboratively brainstorm potential influences on people’s microbiome. Docent explicitly teaches people to create hypotheses by combining personal insights and online learning with task-specific scaffolding. Finally, Galileo reifies experimentation in the software, provides multiple roles for contribution, and automatically manages interdependencies. Multiple evaluations—controlled experiments and field deployments with online communities including American Gut participants—demonstrate that procedural guidance enables people to transform intuitions to hypotheses and structurally-sound experiments. By enabling people to draw on lived experience, this dissertation harbingers a future where people can convert their intuitions to actionable plans and implement these plans with online communities. This dissertation concludes by discussing opportunities for complex work using social computing platforms

People on Drugs: Credibility of User Statements in Health Communities

Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information

Conceptualizing the Electronic Word-of-Mouth Process: What We Know and Need to Know About eWOM Creation, Exposure, and Evaluation

Electronic word of mouth (eWOM) is a prevalent consumer practice that has undeniable effects on the company bottom line, yet it remains an over-labeled and under-theorized concept. Thus, marketers could benefit from a practical, science-based roadmap to maximize its business value. Building on the consumer motivation–opportunity–ability framework, this study conceptualizes three distinct stages in the eWOM process: eWOM creation, eWOM exposure, and eWOM evaluation. For each stage, we adopt a dual lens—from the perspective of the consumer (who sends and receives eWOM) and that of the marketer (who amplifies and manages eWOM for business results)—to synthesize key research insights and propose a research agenda based on a multidisciplinary systematic review of 1050 academic publications on eWOM published between 1996 and 2019. We conclude with a discussion of the future of eWOM research and practice

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Full Issue, Volume 2

Full issue of volume 2

Analyzing collaborative learning processes automatically

In this article we describe the emerging area of text classification research focused on the problem of collaborative learning process analysis both from a broad perspective and more specifically in terms of a publicly available tool set called TagHelper tools. Analyzing the variety of pedagogically valuable facets of learners’ interactions is a time consuming and effortful process. Improving automated analyses of such highly valued processes of collaborative learning by adapting and applying recent text classification technologies would make it a less arduous task to obtain insights from corpus data. This endeavor also holds the potential for enabling substantially improved on-line instruction both by providing teachers and facilitators with reports about the groups they are moderating and by triggering context sensitive collaborative learning support on an as-needed basis. In this article, we report on an interdisciplinary research project, which has been investigating the effectiveness of applying text classification technology to a large CSCL corpus that has been analyzed by human coders using a theory-based multidimensional coding scheme. We report promising results and include an in-depth discussion of important issues such as reliability, validity, and efficiency that should be considered when deciding on the appropriateness of adopting a new technology such as TagHelper tools. One major technical contribution of this work is a demonstration that an important piece of the work towards making text classification technology effective for this purpose is designing and building linguistic pattern detectors, otherwise known as features, that can be extracted reliably from texts and that have high predictive power for the categories of discourse actions that the CSCL community is interested in

Effects of Symbol Sets and Needs Gratifications on Audience Engagement: Contextualizing Police Social Media Communication

We propose a research model based on media synchronicity theory (MST) and examine how the use of different symbol sets (e.g., images and text) is related to audience engagement on social media. We include uses and gratifications theory (UGT) in the model to identify task characteristics that are relevant to message recipients in the specific context of community policing. Based on our analyses of Facebook posts by five police departments, we find first that, consistent with MST, posts conveying information garner more responses when accompanied by more natural symbol sets, and more textual content is preferred to less, but responses differ depending on the type of engagement: intimacy (likes), interaction (comments), or influence (shares). Second, posts intended for meaning convergence gratify the audience’s socialization and assistance needs and are positively related to intimacy and interaction. Finally, the fit between symbol sets and task characteristics impacts different dimensions of audience engagement. These findings provide empirical support for relying on MST when studying social media and for integrating with UGT to capture contextual task characteristics. We conclude the paper with a discussion of the implications of its findings for theory and offer recommendations for practice