Frequent Posters Research Articles

To post or not to Post: How minority opinion and posting frequency impact online review posting

Consumers are often exposed to prior reviews when considering posting themselves. We examine how finding one’s opinion in the minority (vs. majority) of previous ratings affects a consumer’s decision to post. Specifically, we contrast the posting decisions of frequent posters with those of less frequent posters. Results from seven experimental studies show that frequent posters prefer review environments in which their opinion belongs to the minority of previously posted ratings. We support these findings with additional evidence from secondary review data. This behavior sets them apart from infrequent posters, who are reluctant to share minority opinions. In addition, we show that this effect is driven by differences in how a minority post is perceived: As an opportunity to signal one’s reviewer identity or as socially risky. Based on these insights, we explore how rating platforms can motivate review posting, particularly among infrequent posters. Our findings extend previous research on social influence effects in online reviews and have implications for various stakeholders who rely on and aim to solicit reviews on rating platforms.

Gender Communication Differences on a Pediatric Provider Listserv.

Gender-based communication differences are described in educational online communities, but have not been rigorously evaluated in medical online communities. Understanding gender differences in communication may provide insight into gender disparities in the medical profession. Our objective was to describe gender differences in post frequency, content, and language styles on the American Academy of Pediatrics Section on Hospital Medicine (SOHM) listserv. Posts were obtained from publicly available SOHM listserv archives. The first month of every quarter of 2019 and 2020 were reviewed. Two reviewers assigned a post topic (clinical, research, etc) and format (question vs statement) to all deidentified original posts (K = 1.0 topic, 0.89 format). Six trained reviewers assigned language styles (intraclass coefficient = 0.73, indicating good agreement). We analyzed 1592 posts: 287 original posts and 1305 responses. Frequency: Women authored 50% of posts. The 9 most frequent posters (7 men, 2 women) accounted for 19.5% of posts. Content: Men's posts had more words than women's (132.51 vs 112.3, P ≤ .01). Men were more likely to post about health policy and research (P < .001). Men were more likely to post statements compared with women (39% vs 21%, P < .001). Style: Men's posts were more likely to be coded adversarial (12.3% vs 5.5%, P < .001) authoritative (12.2% vs 6.5%, P < .001) or self-amplifying (6.5% vs 3.6%, P < .001). Women contribute disproportionately fewer posts to the American Academy of Pediatrics SOHM listserv compared with their percentage in the subspecialty. We noted significant gender differences in language style and content, which may impact career development and online community inclusion.

Why lurk, why join, and why post? The uses and gratifications of lurkers, infrequent posters, and frequent posters in the brand community context.

Why lurk, why join, and why post? The uses and gratifications of lurkers, infrequent posters, and frequent posters in the brand community context.

Online Brand Communities: The Difference between Lurkers, Infrequent Posters, &amp; Frequent Posters

Brand managers are very interested in getting brand communities right because they lead to many positive outcomes, such as increased brand loyalty, purchase intentions, positive word-of-mouth, and many others. The current module discusses the various types of brand community members. Historically, brand community members have been divided into two types. However, recent research indicates that there are, indeed, three different types of members: lurkers, infrequent posters, and frequent posters. These three types of members are discussed regarding their definition, their motivation when joining these brand communities, and why understanding these differences is important to brand managers.

Why Health Care Professionals Belong to an Intensive Care Virtual Community: Qualitative Study.

BackgroundClinical practice variation that results in poor patient outcomes remains a pressing problem for health care organizations. Some evidence suggests that a key factor may be ineffective internal and professional networks that limit knowledge exchange among health care professionals. Virtual communities have the potential to overcome professional and organizational barriers and facilitate knowledge flow.ObjectiveThis study aimed to explore why health care professionals belong to an exemplar virtual community, ICUConnect. The specific research objectives were to (1) understand why members join a virtual community and remain a member, (2) identify what purpose the virtual community serves in their professional lives, (3) identify how a member uses the virtual community, and (4) identify how members used the knowledge or resources shared on the virtual community.MethodsA qualitative design, underpinned by pragmatism, was used to collect data from 3 asynchronous online focus groups and 4 key informant interviews, with participants allocated to a group based on their posting behaviors during the previous two years—between September 1, 2012, and August 31, 2014: (1) frequent (>5 times), (2) low (≤5 times), and (3) nonposters. A novel approach to focus group moderation, based on the principles of traditional focus groups, and e-moderating was developed. Thematic analysis was undertaken, applying the Diffusion of Innovation theory as the theoretical lens. NCapture (QRS International) was used to extract data from the focus groups, and NVivo was used to manage all data. A research diary and audit trail were maintained.ResultsThere were 27 participants: 7 frequent posters, 13 low posters, and 7 nonposters. All participants displayed an external orientation, with the majority using other social media; however, listservs were perceived to be superior in terms of professional compatibility and complexity. The main theme was as follows: “Intensive care professionals are members of ICUConnect because by being a member of a broader community they have access to credible best-practice knowledge.” The virtual community facilitated access to all professionals caring for the critically ill and was characterized by a positive and collegial online culture. The knowledge found was credible because it was extensive and because the virtual community was moderated and sponsored by a government agency. This enabled members to benchmark and improve their unit practices and keep up to date.ConclusionsThis group of health care professionals made a strategic decision to be members of ICUConnect, as they understood that to provide up-to-date clinical practices, they needed to network with colleagues in other facilities. This demonstrated that a closed specialty-specific virtual community can create a broad heterogeneous professional network, overcoming current ineffective networks that may adversely impact knowledge exchange and creation in local practice settings. To address clinical practice variation, health care organizations can leverage low-cost social media technologies to improve interprofessional and interorganizational networks.

Towards Obesity Surveillance Using Multifaceted Online Social Relational Factors in Reddit

ObjectiveWe aim to better understand online social interactions and environments of individuals interested in weight management from a social media platform called Reddit.IntroductionOverweight and obesity are recognized as one of the greatest modern public health problems1, yet worldwide prevalence of obesity has nearly doubled over the past 30 years2. As part of a strategy to control the obesity pandemic, the WHO recommends an obesity surveillance at the population level3. Empirical studies have shown the importance of social networks in obesity4 and new strategies focusing on social interactions and environments have been proposed5 to prevent the further increase in obesity prevalence. With the increasing use of the internet, online social networks, interactions, and environments (i.e., online social relational factors) deserve more attention.Nearly three- quarters of Americans go online daily6, for functions like connecting with individuals via social network sites7. Like face to face interactions, studies have suggested that social interactions and networks on the internet can influence behavior changes8. Previous studies examining social networking sites typically examine a few selected social networking sites (example studies9,10), although individuals could be members of multiple social networking sites. To better leverage online social relational factors for the purpose of characterizing and monitoring population obesity trends, we investigate weight management community members’ other communities and their level of participation, a first step toward utilizing online multifactorial social interactions and environments.MethodsIn this study, we studied Reddit (http://www.reddit.com), a popular social interaction site, because Reddit hosts many subreddits (i.e., sub-communities), including weight management communities called r/loseit. First, we use a dataset11 — made available on Reddit — that had been used in many informatics studies12–14. For this study, we used a portion of the dataset from Jan 2015 to May 2015. In the first five months of 2015, 5,006,186 members were active in 96,462 subreddits, while submitting 17,851,561 posts and 266,268,920 associated comments. Second, we identified members with more than 3 posts on r/loseit in that period and removed ‘bot’ accounts by manually examining the top 20 frequent posters and their account IDs. Third, we extracted these members’ entire discussions made on Reddit, regardless of the subreddits. Fourth, we identified these members’ overall activities on Reddit and visualized in a network15.ResultsAfter removing bot accounts, we identified 7,734 members who had more than 3 posts in r/loseit from Jan 2015 to May 2015. On average, these members participated in 78.5 subreddits (standard error: 0.1; median: 49.0), while participating in 13,649 unique subreddits as a whole. Members’ participated subreddits are summarized in Figure 1. The size of the nodes represents the number of participating members and the thickness of edges represents the number of members who participated in both subreddits.ConclusionsWe present preliminary findings towards better understanding the online multifactorial social interactions and environments on a social networking site called Reddit. We provide evidence that members encounter many social interactions that occur outside of the community of our interest, the weight management community. However, what members discuss outside of the weight management community as well as the interactions’ influence on weight managements and changes remain unanswered. For example, many members also participate in a subreddit called r/fitness, a community that could share many similar interests with r/loseit. However, the purpose for participating in both communities is unknown. On the basis of our initial analysis, we suggest leveraging online multifaceted social relational factors for the purpose of characterizing and monitoring population obesity trends.

User modeling and usage profiling based on temporal posting behavior in OSNs

In this paper, we study the posting behavior of online social network (OSN) users, in particular the posting frequency and temporal patterns, and consider possible interpretations of how users use the platform. At the aggregate (macro) level, we find two distinct peaks of traffic, one during morning working hours, and one in the evening. The morning peak is more pronounced for frequent posters, while the evening peak is pronounced for the remaining users. We postulate that this difference results from different usage purposes of the OSN platform (e.g. for work, with customers, etc.) than purely social interactions (e.g., friends, family, etc.). We also study user posting behavior at an individual (micro) level and model the user posting sequences as generated by a Hidden Markov Model. We compare the results of using a simple zeroth order model (which is equivalent to a topic model such as LDA), and a first-order model, in terms of their effectiveness in clustering and predicting user types, and show the advantage gained by the first-order HMM. Overall, our study provides new insights into user activity in today’s OSNs, and suggests a framework for profiling users based on their posting activities. We believe our approach will complement other methods of user profiling based on static demographic information and friendship network information.

Content-Sensitive Characterization of Peer Interactions of Highly Engaged Users in an Online Community for Smoking Cessation: Mixed-Methods Approach for Modeling User Engagement in Health Promotion Interventions

BackgroundOnline communities provide affordable venues for behavior change. However, active user engagement holds the key to the success of these platforms. In order to enhance user engagement and in turn, health outcomes, it is essential to offer targeted interventional and informational support.ObjectiveIn this paper, we describe a content plus frequency framework to enable the characterization of highly engaged users in online communities and study theoretical techniques employed by these users through analysis of exchanged communication.MethodsWe applied the proposed methodology for analysis of peer interactions within QuitNet, an online community for smoking cessation. Firstly, we identified 144 highly engaged users based on communication frequency within QuitNet over a period of 16 years. Secondly, we used the taxonomy of behavior change techniques, text analysis methods from distributional semantics, machine learning, and sentiment analysis to assign theory-driven labels to content. Finally, we extracted content-specific insights from peer interactions (n=159,483 messages) among highly engaged QuitNet users.ResultsStudying user engagement using our proposed framework led to the definition of 3 user categories—conversation initiators, conversation attractors, and frequent posters. Specific behavior change techniques employed by top tier users (threshold set at top 3) within these 3 user groups were found to be goal setting, social support, rewards and threat, and comparison of outcomes. Engagement-specific trends within sentiment manifestations were also identified.ConclusionsUse of content-inclusive analytics has offered deep insight into specific behavior change techniques employed by highly engaged users within QuitNet. Implications for personalization and active user engagement are discussed.

Disconfirmation Effect on Online Rating Behavior: A Dynamic Analysis

This research studies the effect of “disconfirmation”—the discrepancy between the expected and experienced assessment of the same product—on the behavior of consumers leaving online product reviews. Specifically, we propose a modeling framework in which an individual’s pre-purchase expectation is shaped by the product ratings she observes and the perception of the review system she holds, both at the time of the purchase. Upon product consumption, the individual obtains the post-purchase evaluation and encounters a certain level of disconfirmation. We extend the Bayesian learning framework to model individual perception of the review system as a subjective attitude underlying how well the aggregate rating signal matches one’s own usage experience. A hierarchical Bayes model is developed and estimated using a rich data set comprising complete purchasing and rating activities on an e-commerce website. Our estimation results show that the relationship between consumer posting propensity and disconfirmation is best described by a U-shaped curve. We also observe several moderating effects: the disconfirmation effect on posting is attenuated by the duration from purchase to receipt of the same product but accentuated by the dissention in product evaluations among peer consumers. A more granular examination reveals that infrequent raters are systematically more susceptible to disconfirmation than frequent posters. The insights from this research lead to actionable strategies for marketers and designers of recommender systems.

Disconfirmation Effect on Online Rating Behavior: A Structural Model

This research studies the effect of disconfirmation—the discrepancy between the expected and experienced assessment of the same product—on the behavior of consumers leaving online product reviews. We propose a modeling framework in which an individual’s prepurchase expectation is shaped by (1) the product ratings she observes and (2) the perception of the review system she has at the time of the purchase. Upon product consumption, the individual obtains the postpurchase evaluation and encounters a certain level of disconfirmation. Drawing on the Bayesian learning framework, we model individual perception of the review system as a subjective attitude underlying how well the aggregate ratings match one’s own usage experience. A hierarchical Bayesian model is developed and estimated using a rich data set comprising complete purchasing and rating activities on an e-commerce website. Our results suggest that an individual’s decisions of whether to post a rating and what rating to post are affected by disconfirmation in two distinct manners. Specifically, an individual is more likely to leave a review when the magnitude of disconfirmation she encounters is larger. In addition, when the individual decides to review a product, the rating she chooses may not neutrally reflect her postpurchase evaluation; the direction of such a bias is in accordance with the sign of disconfirmation. We also observe several moderating effects: the disconfirmation effect on posting is attenuated by the time gap between purchase and receipt of the same product but accentuated by the dissension in product evaluations among peer consumers. A more granular examination reveals that infrequent raters are systematically more susceptible to disconfirmation than frequent posters. The insights from this research lead to actionable strategies for marketers and designers of recommender systems.

Online Product Opinions: Incidence, Evaluation and Evolution

While recent research has demonstrated the impact of online product ratings and reviews on product sales, we still have a limited understanding of the individual’s decision to contribute these opinions. In this research, we empirically model the individual’s decision to provide a product rating and investigate factors that influence this decision. Specifically, we consider how previously posted opinions in a ratings environment may affect a subsequent individual’s posting behavior, both in terms of whether to contribute (incidence) and what to contribute (evaluation), and identify selection effects that influence the incidence decision and adjustment effects that influence the evaluation decision. Our results indicate that individuals vary in their underlying behavior and in their reactions to previously posted product ratings. While less frequent posters exhibit bandwagon behavior, more active customers reveal differentiation behavior when posting online opinions. Systematic patterns in these behaviors have important implications for the evolution of online product opinions, which we illustrate through the use of simulations. Our simulations also show that posted product opinions can be affected substantially by the composition of the underlying customer base. Specifically, when a product’s customers are polarized, posted opinions are more negative and exhibit a stronger downward trend when compared to a homogeneous, neutral customer base with the same median opinion. This is a result of a core group of “activist” customers posting increasingly negative opinions in an effort to differentiate themselves from others in the community.

Online Product Opinions: Incidence, Evaluation, and Evolution

Whereas recent research has demonstrated the impact of online product ratings and reviews on product sales, we still have a limited understanding of the individual's decision to contribute these opinions. In this research, we empirically model the individual's decision to provide a product rating and investigate factors that influence this decision. Specifically, we consider how previously posted ratings may affect an individual's posting behavior in terms of whether to contribute (incidence) and what to contribute (evaluation), and we identify selection effects that influence the incidence decision and adjustment effects that influence the evaluation decision. Across individuals, our results show that positive ratings environments increase posting incidence, whereas negative ratings environments discourage posting. Our results also indicate important differences across individuals in how they respond to previously posted ratings, with less frequent posters exhibiting bandwagon behavior and more active customers revealing differentiation behavior. These dynamics affect the evolution of online product opinions. Through simulations, we illustrate how the evolution of posted product opinions is shaped by the underlying customer base and show that customer bases with the same median opinion may evolve in substantially different ways because of the presence of a core group of “activists” posting increasingly negative opinions.

Biomedtalk's Top Posters: The Faces Behind the Postings

Biomedtalk's Top Posters: The Faces Behind the Postings

Psychological Barriers: Lurker and Poster Motivation and Behavior in Online Communities

Virtual communities provide an attractive place for organizations to mine information regarding customer perceptions, needs, and demographics; as well as to generate revenue through sales of products, services, information, and advertising. However, the community conversation provides information about only one type of community user, the poster. Information about the lurker, who never posts, is conspicuously absent from the obvious community data source, the postings. Lurkers may be a large portion of the user community and could provide key revenue sources and vital information, or they potentially could turn into posters. This research contrasts the differences in the underlying motivations of lurkers, infrequent posters, and posters in order to understand the resulting differences in their behavior. 518 users from 20 virtual communities were categorized into three groups based upon their posting behaviors: lurkers who never posted, infrequent posters who posted three or less times per month, and frequent posters. Results revealed that lurkers differed significantly from posters, especially in their willingness to give information and exchange social support. There was a gradual progression from lurker to poster regarding the desires to get knowledge and obtain shopping information. Implications about a possible psychological barrier regarding giving information and social support are discussed.