PFU: Profiling Forum users in online social networks, a knowledge driven data mining approach

Online Social Networks (OSNs) provide platform to raise opinions on various issues, create and spread news rapidly in Online Social Network Forums (OSNFs). This work proposes a novel method for Profiling Forum Users (PFU) by exploring their behavioral characteristics based on their involvement in various topics of discussion and number of posts in respective topics posted by them in OSNFs dynamically. Modeling the proposed method mathematically, the PFU algorithm is illustrated for its adequacy and accuracy

Visualizing Collective Discursive User Interactions in Online Life Science Communities

This paper highlights the rationale for the development of BioViz, a tool to help visualize the existence of collective user interactions in online life science communities. The first community studied has approximately 22,750 unique users and the second has 35,000. Making sense of the number of interactions between actors in these networks in order to discern patterns of collective organization and intelligent behavior is challenging. One of the complications is that forums - our object of interest - can vary in their purpose and remit (e.g. the role of gender in the life sciences to forums of praxis such as one exploring the cell line culturing) and this shapes the structure of the forum organization itself. Our approach took a random sample of 53 forums which were manually analyzed by our research team and interactions between actors were recorded as arcs between nodes. The paper focuses on a discussion of the utility of our approach, but presents some brief results to highlight the forms of knowledge that can be gained in identifying collective group formations. Specifically, we found that by using a matrix-based visualization approach, we were able to see patterns of collective behavior which we believe is valuable both to the study of collective intelligence and the design of virtual organizations.Comment: Presented at Collective Intelligence conference, 2012 (arXiv:1204.2991

Adding dimensions to the analysis of the quality of health information of websites returned by Google. Cluster analysis identifies patterns of websites according to their classification and the type of intervention described.

Background and aims: Most of the instruments used to assess the quality of health information on the Web (e.g. the JAMA criteria) only analyze one dimension of information quality, trustworthiness. We try to compare these characteristics with the type of treatments the website describe, whether evidence-based medicine or note, and correlate this with the established criteria. Methods: We searched Google for “migraine cure” and analyzed the first 200 websites for: 1) JAMA criteria (authorship, attribution, disclosure, currency); 2) class of websites (commercial, health portals, professional, patient groups, no-profit); and 3) type of intervention described (approved drugs, alternative medicine, food, procedures, lifestyle, drugs still at the research stage). We used hierarchical cluster analysis to assess associations between classes of websites and types of intervention described. Subgroup analysis on the first 10 websites returned was performed. Results: Google returned health portals (44%), followed by commercial websites (31%) and journalism websites (11%). The type of intervention mentioned most often was alternative medicine (55%), followed by procedures (49%), lifestyle (42%), food (41%) and approved drugs (35%). Cluster analysis indicated that health portals are more likely to describe more than one type of treatment while commercial websites most often describe only one. The average JAMA score of commercial websites was significantly lower than for health portals or journalism websites, and this was mainly due to lack of information on the authors of the text and indication of the date the information was written. Looking at the first 10 websites from Google, commercial websites are under-represented and approved drugs over-represented. Conclusions: This approach allows the appraisal of the quality of health-related information on the Internet focusing on the type of therapies/prevention methods that are shown to the patient

Predictive Modelling Using Unstructured Data From Online Forums: A Case Study on E-cigarette Users

In the age of the digital economy, social media, forums and other online platforms have played active parts in our daily activities. The amount of data digitized and recorded in these platforms have surged exponentially. Many believed that this underexplored unstructured data sources have huge potential in offering insights to policy makers and companies. This paper aims to propose a hybrid approach using inductive and deductive reasoning to identify motivational factors to use e-cigarettes for predictive modelling. A total of 790 comments and discussions relevant to e-cigarette use and motivations to use e-cigarette were scraped and stored from online forums like Reddit, Vapingunderground and e-cigarette-forum. A series of text analytics were conducted on the text corpus and the cluster analysis enabled us to build a predictive model. Using Bayesian Structural Equation Modelling, we concluded that the constructs derived by clustering, i.e. Cost and Convenience and Enjoyment, have significant associations with smokers trying to quit smoking. While health-related issues were inherent to the notion of quitting smoking, enjoyment, cost and convenience were motivational factors which will generate favourable response towards quitting smoking. The findings showed encouraging results from a methodological standpoint and offered insights to policy makers and companies on health-related issues pertaining to the use of e-cigarettes

Evaluating Online forums as a Customer Service Tool for Consumer Products

In this study, we capture communication activities on 14 online forums that are implemented primarily to provide customer service for consumers of mobile phones and MP3 players. We perform a large scale analysis to investigate the social nature and the effectiveness of using online forums as a customer service tool. Unlike previous studies, our research examines a complete archive of the actual communication activities on online forums that are publicly available to general consumers. Our comparative and social network analysis allows us to compare the structural differences among these social networks

Understanding patient needs and gaps in radiology reports through online discussion forum analysis

Our objective is to investigate patient needs and understand information gaps in radiology reports using patient questions that were posted on online discussion forums. We leveraged online question and answer platforms to collect questions posted by patients to understand current gaps and patient needs. We retrieved six hundred fifty-nine (659) questions using the following sites: Yahoo Answers, Reddit.com, Quora, and Wiki Answers. The questions retrieved were analyzed and the major themes and topics were identified. The questions retrieved were classified into eight major themes. The themes were related to the following topics: radiology report, safety, price, preparation, procedure, meaning, medical staff, and patient portal. Among the 659 questions, 35.50% were concerned with the radiology report. The most common question topics in the radiology report focused on patient understanding of the radiology report (62 of 234 [26.49%]), image visualization (53 of 234 [22.64%]), and report representation (46 of 234 [19.65%]). We also found that most patients were concerned about understanding the MRI report (32%; n = 143) compared with the other imaging modalities (n = 434). Using online discussion forums, we discussed major unmet patient needs and information gaps in radiology reports. These issues could be improved to enhance radiology design in the future