Infer user interests via link structure regularization

Learning user interests from online social networks helps to better understand user behaviors and provides useful guidance to design user-centric applications. Apart from analyzing users' online content, it is also important to consider users' social connections in the social Web. Graph regularization methods have been widely used in various text mining tasks, which can leverage the graph structure information extracted from data. Previously, graph regularization methods operate under the cluster assumption that nearby nodes are more similar and nodes on the same structure (typically referred to as a cluster or a manifold) are likely to be similar. We argue that learning user interests from complex, sparse, and dynamic social networks should be based on the link structure assumption under which node similarities are evaluated based on the local link structures instead of explicit links between two nodes. We propose a regularization framework based on the relation bipartite graph, which can be constructed from any type of relations. Using Twitter as our case study, we evaluate our proposed framework from social networks built from retweet relations. Both quantitative and qualitative experiments show that our proposed method outperforms a few competitive baselines in learning user interests over a set of predefined topics. It also gives superior results compared to the baselines on retweet prediction and topical authority identification

Social Networks for Language Learning

Social networks play indispensable roles in fostering second language learning by providing a wide array of authentic materials. The purpose of this review is to consider social networks, such as Facebook, Electronic mail, Computer media which are proven to be effective to increase students’ learning English out of the classes. Social networks facilitate students’ interaction to share their ideas, and provide an opportunity for learners to experience online tools to foster their learning skills. It was realized that these online tools (e.g. Facebook, Email, and Computer media) can be used to improve students’ language skills especially writing skill. Internet tools help the second language learners to accelerate their learning by being up-to-date and self-directed. In this paper, the literatures were reviewed to find positive aspects of using Facebook to improve second language learning. The researchers also pointed out that second language is learned incidentally and directly from second language speakers of different culture via Emails. Students can use e-mail to communicate with their teachers and with second language speakers or native speakers.  Computer media are also useful means to guide those learners who are passively focused on English learning. The review would conclude that social interaction via social networks is a kind of stimulus for learners to communicate with others

Stochastic Sampling and Machine Learning Techniques for Social Media State Production

The rise in the importance of social media platforms as communication tools has been both a blessing and a curse. For scientists, they offer an unparalleled opportunity to study human social networks. However, these platforms have also been used to propagate misinformation and hate speech with alarming velocity and frequency. The overarching aim of our research is to leverage the data from social media platforms to create and evaluate a high-fidelity, at-scale computational simulation of online social behavior which can provide a deep quantitative understanding of adversaries\u27 use of the global information environment. Our hope is that this type of simulation can be used to predict and understand the spread of misinformation, false narratives, fraudulent financial pump and dump schemes, and cybersecurity threats. To do this, our research team has created an agent-based model that can handle a variety of prediction tasks. This dissertation introduces a set of sampling and deep learning techniques that we developed to predict specific aspects of the evolution of online social networks that have proven to be challenging to accurately predict with the agent-based model. First, we compare different strategies for predicting network evolution with sampled historical data based on community features. We demonstrate that our community-based model outperforms the global one at predicting population, user, and content activity, along with network topology over different datasets. Second, we introduce a deep learning model for burst prediction. Bursts may serve as a signal of topics that are of growing real-world interest. Since bursts can be caused by exogenous phenomena and are indicative of burgeoning popularity, leveraging cross-platform social media data is valuable for predicting bursts within a single social media platform. An LSTM model is proposed in order to capture the temporal dependencies and associations based upon activity information. These volume predictions can also serve as a valuable input for our agent-based model. Finally, we conduct an exploration of Graph Convolutional Networks to investigate the value of weak-ties in classifying academic literature with the use of graph convolutional neural networks. Our experiments look at the results of treating weak-ties as if they were strong-ties to determine if that assumption improves performance. We also examine how node removal affects prediction accuracy by selecting nodes according to different centrality measures. These experiments provide insight for which nodes are most important for the performance of targeted graph convolutional networks. Graph Convolutional Networks are important in the social network context as the sociological and anthropological concept of \u27homophily\u27 allows for the method to use network associations in assisting the attribute predictions in a social network

A Survey on Studying the Social Networks of Students

Do studies show that physical and online students' social networks support education? Analyzing interactions between students in schools and universities can provide a wealth of information. Studies on students' social networks can help us understand their behavioral dynamics, the correlation between their friendships and academic performance, community and group formation, information diffusion, and so on. Educational goals and holistic development of students with various academic abilities and backgrounds can be achieved by incorporating the findings attained by the studies in terms of knowledge propagation in classroom and spread of delinquent behaviors. Moreover, we use Social Network Analysis (SNA) to identify isolated students, ascertain the group study culture, analyze the spreading of various habits like smoking, drinking, and so on. In this paper, we present a review of the research showing how analysis of students' social networks can help us identify how improved educational methods can be used to make learning more inclusive at both school and university levels and achieve holistic development of students through expansion of their social networks, as well as control the spread of delinquent behaviors.Comment: Huso 201

TEACHERS’ CONCEPTION TOWARDS THE USE OF SOCIAL NETWORKS AS A TOOL FOR PROFESSIONAL DEVELOPMENT IN TANZANIA GOVERNMENT SECONDARY SCHOOLS: THE CASE OF DODOMA MUNICIPALITY, TANZANIA

The study sought to assess teachers’ conception towards the use of social networks as a tool for professional development in Tanzania government secondary schools in Dodoma Municipality. Thus, the specific objectives of this study were to assess teachers’ conception on the available social networks opportunities that can support professional development, and to examine the limitations that hinder teachers’ use of social networks available for professional development. The paper is guided by the social-cognitive theory which stresses that learning takes place in a social environment. A cross-sectional  research design  was  used  to  collect  data  that  involved 84  teachers  from  ten secondary schools, six heads of schools, three quality assurers and one respondent from District education office. Qualitative data were analyzed through content analysis and quantitative data were descriptively analyzed through SPSS Version 20 of which the mean score was obtained. The survey results indicated teachers had positive conceptions towards the use of social networks as a tool for professional development. Furthermore, the findings revealed that teachers faced several challenges which include lack training on how to integrate SNs in TPD and high costs of the internet bandwidth, just to mention a few. Finally, the researchers recommend that, teachers should be exposed to professional development programmes that empower them to develop various pedagogical skills and understand a variety of learning environment that can improve their practice through collaborative online social networks. Again, teachers should be provided with opportunities to use the available SNs to create professional learning networks in their local context and globally.  Article visualizations

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

An assessment into the impact of using on-line social networking in enhancing learning at University of Malawi - Kamuzu College of Nursing

Quality Management is a critical success factor for organizations to grow their business. Though for a long time, quality management has always been attributed to industries that produce physical products which normally undergo quality inspection at the end of the line, there has been a shift in the trend in recent times where quality management strategies are being applied in service delivery industries as well. One of the service delivery industries is the education sector and especially higher learning institutions like UNIMA – KCN in particular. In the TQM model of quality management, one of its strategies namely Deming Theory says that; to implement quality management in service delivery there is a need to look at how the processes and systems of an organization work, understanding the changes taking place and its causal factors and understanding the human nature and how they are influenced (Knowles, 2011). At KCN one of the key processes for the success of the service delivery is the teaching and learning process which for a long time has used the traditional classroom style of delivery. However, there is a new trend of technology that has come into play in recent years and it is required that institution of higher learning should adopt these technology applications to enhance its processes of service delivery in order to improve quality. This study utilized a case study research strategy under qualitative research method to assess the impact of using online social networking in enhancing learning and teaching at KCN as part of quality management. Data was collected from students and lecturers using a questionnaire which assessed the current dominant learning and teaching styles at KCN, the perception of students and lecturers on use of social networks, challenges facing the adoption of social networks for learning and the proposed strategies that can assist in adopting the use of social networks for learning. The collected data was analyzed using qualitative analysis method with Survey Monkey tool and the results clearly show that social networks have a positive impact on learning and teaching process. The results further confirm that KCN is ready to adopt the use of social networks for learning as it possesses the required teaching and learning styles and it has positive perception towards social networks. The study recommends that a further research be conducted to ascertain the suggested social sites and the strategy of implementing the use of social networks for learning. This could be done with the use of a survey strategy and probability sampling to ensure all data is properly captured from students and lecturers

Evolution of corporate reputation during an evolving controversy

Purpose: The purpose of this paper is to investigate the evolution of online sentiments toward a company (i.e. Chipotle) during a crisis, and the effects of corporate apology on those sentiments. Design/methodology/approach: Using a very large data set of tweets (i.e. over 2.6m) about Company A’s food poisoning case (2015–2016). This case was selected because it is widely known, drew attention from various stakeholders and had many dynamics (e.g. multiple outbreaks, and across different locations). This study employed a supervised machine learning approach. Its sentiment polarity classification and relevance classification consisted of five steps: sampling, labeling, tokenization, augmentation of semantic representation, and the training of supervised classifiers for relevance and sentiment prediction. Findings: The findings show that: the overall sentiment of tweets specific to the crisis was neutral; promotions and marketing communication may not be effective in converting negative sentiments to positive sentiments; a corporate crisis drew public attention and sparked public discussion on social media; while corporate apologies had a positive effect on sentiments, the effect did not last long, as the apologies did not remove public concerns about food safety; and some Twitter users exerted a significant influence on online sentiments through their popular tweets, which were heavily retweeted among Twitter users. Research limitations/implications: Even with multiple training sessions and the use of a voting procedure (i.e. when there was a discrepancy in the coding of a tweet), there were some tweets that could not be accurately coded for sentiment. Aspect-based sentiment analysis and deep learning algorithms can be used to address this limitation in future research. This analysis of the impact of Chipotle’s apologies on sentiment did not test for a direct relationship. Future research could use manual coding to include only specific responses to the corporate apology. There was a delay between the time social media users received the news and the time they responded to it. Time delay poses a challenge to the sentiment analysis of Twitter data, as it is difficult to interpret which peak corresponds with which incident/s. This study focused solely on Twitter, which is just one of several social media sites that had content about the crisis. Practical implications: First, companies should use social media as official corporate news channels and frequently update them with any developments about the crisis, and use them proactively. Second, companies in crisis should refrain from marketing efforts. Instead, they should focus on resolving the issue at hand and not attempt to regain a favorable relationship with stakeholders right away. Third, companies can leverage video, images and humor, as well as individuals with large online social networks to increase the reach and diffusion of their messages. Originality/value: This study is among the first to empirically investigate the dynamics of corporate reputation as it evolves during a crisis as well as the effects of corporate apology on online sentiments. It is also one of the few studies that employs sentiment analysis using a supervised machine learning method in the area of corporate reputation and communication management. In addition, it offers valuable insights to both researchers and practitioners who wish to utilize big data to understand the online perceptions and behaviors of stakeholders during a corporate crisis

The use of micro-blogging for teacher professional development support and personalized professional learning

The purpose of this qualitative study was to look at how teachers use micro-blogging, in this case Twitter (www.twitter.com), for their own personalized professional learning and how effective Twitter is as a professional development (PD) tool. In order to measure the effectiveness of the tool, the researcher first gleaned nine essential characteristics of effective PD from the literature. This list was validated by experts in the PD community. The significance of this study was to reveal how participants actually used Twitter for PD, what their perspectives on the tool were, and how effective their experiences were with Twitter as a PD tool. Results of this study can be used to improve current practice, and provide a low cost, accessible, and available mechanism to foster an on-going, learner-centered, approach to PD, thus allowing teachers to become more involved in their own professional growth. For the 4 participants in this study, Twitter use for PD and its effectiveness varied greatly. The effectiveness of the tool depended on the participant’s fluency with the technology and attitude towards social media. For the most fluent participant, Twitter met most of the requirements for effectiveness; however, Twitter use did not automatically provide a mechanism for reflection or self-assessment; nor did Twitter use provide an evaluation of the experience, both requirements of effective PD. With added evaluation and self-assessment processes, and with a fluent practitioner, Twitter does have the potential to be a very effective PD tool with its low cost, accessibility, and availability