Moderated Online Communities

Online communities provide a social sphere for people to share information and knowledge. While information sharing is becoming a ubiquitous online phenomenon, how to ensure information quality or induce quality content, however, remains a challenge due to the anonymity of commentators. This paper introduces moderation into reputation systems. We show that moderation directly impacts strategic commentators ’ incentive to generate useful information, and moderation is generally desirable to improve information quality. Interestingly, we find that when being moderated with different probabilities based on their reputations, commentators may display a pattern of reputation oscillation, in which they generate useful content to build up high reputation and then exploit their reputation. As a result, the expected performance from highreputation commentators can be inferior to that from low-reputation ones (reversed reputation). We finally investigate the optimal moderation resource allocation, and conclude that the seemingly abnormal reversed reputation could arise as an optimal result

Moderated Online Communities and User-Generated Content

Online communities provide a social sphere for people to share information and knowledge. While information sharing is becoming a ubiquitous online phenomenon, how to ensure information quality or induce quality content, however, remains a challenge due to the anonymity of commentators. This paper introduces moderation into reputation systems. We show that moderation directly impacts strategic commentators incentive to generate useful information, and moderation is generally desirable to improve information quality. Interestingly, we find that when being moderated with different probabilities based on their reputations, commentators may display a pattern of reputation oscillation, in which they generate useful content to build up high reputation and then exploit their reputation. As a result, the expected performance from high-reputation commentators can be inferior to that from low-reputation ones (reversed reputation). We then investigate the optimal moderation resource allocation, and conclude that the seemingly abnormal reversed reputation could arise as an optimal result. The paper concludes with a discussion of the development of a scientific moderation system with application to academic publishing

When Do Likes Create Bias?

The rise of online communities has ushered in a new era of content sharing with platforms that serve many functions and overcome the geographic and synchronous limitations of traditional word-of-mouth communications. Community-based question answering sites (CQA) have emerged as convenient platforms for users to exchange knowledge and opinions with others. Research on CQA has primarily focused on engaging members to voluntarily contribute to these communities. Helpfulness ratings and “likes” are one mechanism platforms can use to engage members, but these subjective evaluations can also create bias. In this ERF paper, the elaboration likelihood model is applied to better understand when bias can occur with these platforms. An experimental design and a planned data collection are reported

Learning in the wild:Predicting the formation of ties in ‘Ask’ subreddit communities using ERG models

The theoretical lenses, empirical measures and analytical tools associated with social network analysis comprise a wealth of knowledge that can be used to analyse networked learning. This has popularized the use of the social network analysis approach to understand and visualize structures and dynamics in online learning networks, particularly where data could be automatically gathered and analysed. Research in the field of social network learning analysis has (a) used social network visualizations as a feedback mechanism and an intervention to enhance online social learning activities (Bakharia & Dawson, 2011; Schreurs, Teplovs, Ferguson, de Laat, & Buckingham Shum, 2013), (b) investigated what variables predicted the formation of learning ties in networked learning processes (Cho, Gay, Davidson, & Ingraffea, 2007), (c) predicted learning outcomes in online environments (Russo & Koesten, 2005), and (d) studied the nature of the learning ties (de Laat, 2006). This paper expands the understanding of the variables predicting the formation of learning ties in online informal environments. Reddit, an online news sharing site that is commonly referred to as ‘the front page of the Internet’, has been chosen as the environment for our investigation because conversations on it emerge from the contributions of members, and it combines perspectives of experts and non-experts (Moore & Chuang, 2017) taking place in a plethora of subcultures (subreddits) occurring outside traditional settings. We study two subreddit communities, ‘AskStatistics’, and ‘AskSocialScience’, in which we believe that informal learning is likely to happen in Reddit, and which offer avenues for comparison both in terms of the communication dynamics and learning processes occurring between members. We gathered all the interactions amongst the users of these two subreddit communities for a 1-year period, from January 1st, 2015 until December 31st, 2015. Exponential Random Graph models (ERGm) were employed to determine the endogenous (network) and exogenous (node attributes) factors facilitating the networked ties amongst the users of these communities. We found evidence that Redditors’ networked ties arise from network dynamics (reciprocity and transitivity) and from the Redditors’ role as a moderator in the subreddit communities. These results shed light into the understanding of the variables predicting the formation of ties in informal networked learning environments, and more broadly contribute to the development of the field of social network learning analysis

Can AI Moderate Online Communities?

The task of cultivating healthy communication in online communities becomes increasingly urgent, as gaming and social media experiences become progressively more immersive and life-like. We approach the challenge of moderating online communities by training student models using a large language model (LLM). We use zero-shot learning models to distill and expand datasets followed by a few-shot learning and a fine-tuning approach, leveraging open-access generative pre-trained transformer models (GPT) from OpenAI. Our preliminary findings suggest, that when properly trained, LLMs can excel in identifying actor intentions, moderating toxic comments, and rewarding positive contributions. The student models perform above-expectation in non-contextual assignments such as identifying classically toxic behavior and perform sufficiently on contextual assignments such as identifying positive contributions to online discourse. Further, using open-access models like OpenAI's GPT we experience a step-change in the development process for what has historically been a complex modeling task. We contribute to the information system (IS) discourse with a rapid development framework on the application of generative AI in content online moderation and management of culture in decentralized, pseudonymous communities by providing a sample model suite of industrial-ready generative AI models based on open-access LLMs

Strategic Digital Campaign to Improve Rural Health Workers Recruitment Process in Indonesia

With current geographical disparities, Indonesia's ratio of general practitioners (GPs) to population is still lower than the WHO-recommended figure. The Center for Indonesia's Strategic Initiatives (CISDI) initiated Pencerah Nusantara (PN), a team-based young health workers deployment program to rural areas to improve the distribution of human resources for health including GPs, nurses, midwives, public health specialists, and health advocates. Entering PN's sixth year of implementation, aimed to attract more application from young health workers, particularly of GPs, CISDI employed strategic digital campaign involving strategic content development, strategic content channeling and strategic content promotion; as an intervention to improve the recruitment process of PN Batch 6. This paper investigates whether the intervention manages to improve the recruitment process of PN and mainly using secondary data such as “Relative Volume Search” measuring Google search popularity, social media insights measuring social media engagement and Google Analytics of PN weblog measuring weblog visits and online recruitment data measuring PN daily application rate from 2016 and 2017. Following the intervention, Google search popularity was doubled, social media engagement showed improvement range from 153 percent to 1,813 percent and PN daily application rate increased 148 percent of health workers and 192 percent of GP, compared to 32 percent and 44 percent of 2016 accordingly. A specifically targeted digital campaign implemented substantially improved recruitment promotion indicated by the significant growth of PN daily application rate.&nbsp

Social Media Influencers: Talk is Not Cheap!

Social media influencers are digital opinion leaders who have amassed large followings on social media. Through their content and communication on social commerce platforms, and social networking sites, social media influencers can influence their audience\u27s attitude towards brands and encourage purchase decisions. This paper explores the impact of social media influencers on the relationship between social commerce and purchase intention. Since the relationship between social commerce and purchase intention has been thoroughly examined by researchers, I discover through reviewing the literature how an independent entity can impact social media consumers\u27 attitudes and purchase decisions. Specifically, I analyze the social commerce construct and develop propositions related to the relationships between the dimensions of social commerce and the social media influencer construct

Building Organizational Knowledge Quality: Investigating the Role of Social Media and Social Capital

To the extent that knowledge is the most strategically important resource for sustainable competitive advantage, organizations must consciously and systematically manage their knowledge asset. In this paper, we explore how social media and social capital at organizational level help organizations benefit from their knowledge management initiatives through improving organizational knowledge quality. A research model was developed and survey data were used to test the model. The preliminary results show that social media helps to provide the technical environment conducive to knowledge exchange and social capital enables the actual knowledge sharing between businesses. Both facilitate an organizational emphasis on knowledge management, which leads to organizational knowledge of higher quality