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

Social media and social capital in online learning

Abstract : Online learning inherently affords collaborative learning opportunities for participating students. Open distance learning (ODL) institutions typically accommodate students from diverse educational backgrounds with disparate levels of access to technological resources. The mere existence of an online learning platform does not necessarily equate to student access to collaborative learning opportunities. A qualitative study investigated how diverse students in an online learning module collaborated with peers in furthering their learning project at a large ODL university. It emerges that students engage in various formal and informal collaborative learning activities which constitute the creation of personal learning environments (PLEs). PLEs demonstrate the role of student agency as students coordinate their options. Social capital theory shows how different types of social ties in PLEs provide for bonding and bridging social capital; the combination of which serves the learning project by providing for both strong ties in supportive relationships between students and weak ties with knowledge generation capabilities between previously unacquainted students. The results can assist online learning practitioners who wish to promote beneficial collaborative learning opportunities among their students

Transforming Online Ties in Tools for Entrepreneurial Learning Readiness in Small Transition Economies

Online social networks create the opportunity to increase and expand network ties, they provide a new channel that widens weak ties and intensifies stronger ties. Broad and diverse social network is linked to entrepreneurial success. Learning is an essential dimension of entrepreneurial network. Entrepreneurs may benefit from expertise and they can exploit future entrepreneurial learning opportunities. This paper explores the entrepreneurial learning leverage that young students enrolled in higher education system can get from online ties in small transition economies focusing in Western Balkan region and more precisely in Albania comparing with a small-developed county such as Estonia. The paper explains how young student are ready to use online ties for entrepreneurial opportunity recognition. Further online learning strategies are explored through focus group analysis and blog analysis. Young students use online ties for entrepreneurial knowledge sharing. The study concludes with the suggestion of a typology of entrepreneurial learning orientation strategies

Comparing Online Social Networks Ties as Tool for Entrepreneurial Learning Readiness in Small Economies

Online social networks such as Facebook and LinkedIn create the opportunity to expand online and face-to-face ties. Diverse online social network composed by weak and strong ties is essential for the young student entrepreneurs. Online networks off diverse learning and online ex-pertize opportunities to the young student entrepreneur. This paper explores the entrepreneurial learning leverage that young students enrolled in higher education system can get from online ties in small economies through comparing Western Balkan region and more precisely a small developing country such as Albania with a small-developed county such as Estonia. The paper explores how online ties support young student readiness to use online networking platforms for online entrepreneurial learning and entrepreneurial opportunity recognition focusing in online ties established in Facebook and LinkedIn. Further online learning strategies are explored through focus group analysis, blog analysis and interviews with young experienced entrepreneurs. Young students use online ties for entrepreneurial knowledge sharing but there a difference between the Facebook tie and the LinkedIn tie. The study concludes with the suggestion of development of entrepreneurial learning orientation strategies and tools that facilitate the online learning process in both online networks focusing in specific online tie that is the group tie

AGENT: Alumni growth and engagement across new technologies

The AGENT project aims to use social networking technologies (LinkedIn and Facebook) to support the development of undergraduates’ employability and career development. The focus of the project is on e-mentoring by alumni to provide a ‘bridge’ between individuals whose social ties and connections are weakened by time and distance, whilst at the same time capitalising on the learning opportunities afforded by the widening of social networks. Social networking sites (SNSs) have been shown to provide students with the tools to make connections, build relationships and support personal development. From a social capital perspective, SNSs can support ‘weak ties’ by allowing students to grow a social network from which to draw resources in the form of information, knowledge, advice and expertise that an institution’s alumni can provide. Alumni are a key resource for building professional networking communities that can provide offline as well as online support to students. We report on an on-going JISC project –AGENT (Alumni Growth and Engagement across New Technologies) that explores how Web 2.0 technologies engage alumni, create the sense of belonging, develop more effective and mutually-beneficial alumni-student connections and associated positive social capital outcomes.Joint Information Systems Committee (JISC) 2011-201

Multi-Dimensional Observational Learning in Social Networks: Theory and Experimental Evidence

Over the last decade, there has been an unprecedented growth of social media platforms (e.g., Instagram, and Pinterest). This growth has resulted in significant increases in the availability of individual-specific information such as holiday pictures, mobile check-ins at restaurants, to everyday purchases. Besides sharing their data, about 72% of Instagram users also made purchase decisions after seeing something on Instagram, with the most common categories being clothing, makeup, shoes, and jewelry . In a similar vein, Pinterest, an image-based social platform, has product rich pins that facilitate users to discover new products: In 2016, 55% of U.S. online users shared that their primary use of Pinterest was to find and shop for products . The prevalence of consumers sharing their purchases on social media platforms (e.g., Instagram, and Pinterest) and the use of this information by potential future consumers have substantial implications for online retailing. Consumers can observe the purchase information shared by their “friends” or “strangers.” The product offerings on a social network are diverse and led us to integrate products with different attributes. One approach to classify product types by distinct characteristics is to consider how these attributes drive consumer choice, i.e., classification of products with vertically and horizontally differentiated attributes. In this study, we examine how product characteristics and the type of information provider jointly moderate the purchase decision in a social network setting. We first propose an analytical observational learning framework integrating the impact of product differentiation and social ties. Then, we use two experimental studies to validate our analytical results and provide additional insights. Our key findings are that the effect of learning from strangers is stronger for vertically differentiated products than for the horizontally differentiated product. However, the impact of learning from friends does not depend on whether the underlying product is horizontally or vertically differentiated. What is more interesting is the nuanced role of social ties: For horizontally differentiated products, the effect of learning increases with the strength of social ties. In addition, “contact-based” tie strength is more important than “structure-based” tie strength in accelerating observational learning. These findings can motivate online retailers to generate alternative strategies for increasing product sales through social networks. For example, online retailers offering horizontally differentiated products have strong incentives to cooperate with social media platforms (e.g., Instagram and Pinterest) in encouraging customers to share their purchase information

Modeling Paying Behavior in Game Social Networks

Online gaming is one of the largest industries on the Internet, generating tens of billions of dollars in revenues annually. One core problem in online game is to find and convert free users into paying customers, which is of great importance for the sustainable development of almost all online games. Although much research has been conducted, there are still several challenges that remain largely unsolved: What are the fundamental factors that trigger the users to pay? How does users? paying behavior influence each other in the game social network? How to design a prediction model to recognize those potential users who are likely to pay? In this paper, employing two large online games as the basis, we study how a user becomes a new paying user in the games. In particular, we examine how users' paying behavior influences each other in the game social network. We study this problem from various sociological perspectives including strong/weak ties, social structural diversity and social influence. Based on the discovered patterns, we propose a learning framework to predict potential new payers. The framework can learn a model using features associated with users and then use the social relationships between users to refine the learned model. We test the proposed framework using nearly 50 billion user activities from two real games. Our experiments show that the proposed framework significantly improves the prediction accuracy by up to 3-11% compared to several alternative methods. The study also unveils several intriguing social phenomena from the data. For example, influence indeed exists among users for the paying behavior. The likelihood of a user becoming a new paying user is 5 times higher than chance when he has 5 paying neighbors of strong tie. We have deployed the proposed algorithm into the game, and the Lift_Ratio has been improved up to 196% compared to the prior strategy

Why it is important to consider negative ties when studying polarized debates: A signed network analysis of a Dutch cultural controversy on Twitter

Despite the prevalence of disagreement between users on social media platforms, studies of online debates typically only look at positive online interactions, represented as networks with positive ties. In this paper, we hypothesize that the systematic neglect of conflict that these network analyses induce leads to misleading results on polarized debates. We introduce an approach to bring in negative user-to-user interaction, by analyzing online debates using signed networks with positive and negative ties. We apply this approach to the Dutch Twitter debate on ‘Black Pete’—an annual Dutch celebration with racist characteristics. Using a dataset of 430,000 tweets, we apply natural language processing and machine learning to identify: (i) users’ stance in the debate; and (ii) whether the interaction between users is positive (supportive) or negative (antagonistic). Comparing the resulting signed network with its unsigned counterpart, the retweet network, we find that traditional unsigned approaches distort debates by conflating conflict with indifference, and that the inclusion of negative ties changes and enriches our understanding of coalitions and division within the debate. Our analysis reveals that some groups are attacking each other, while others rather seem to be located in fragmented Twitter spaces. Our approach identifies new network positions of individuals that correspond to roles in the debate, such as leaders and scapegoats. These findings show that representing the polarity of user interactions as signs of ties in networks substantively changes the conclusions drawn from polarized social media activity, which has important implications for various fields studying online debates using network analysis