Communities In Online Social Networks Research Articles

Text Mining - A Comparative Review of Twitter Sentiments Analysis

Background: Text mining derives information and patterns from textual data. Online social media platforms, which have recently acquired great interest, generate vast text data about human behaviors based on their interactions. This data is generally ambiguous and unstructured. The data includes typing errors and errors in grammar that cause lexical, syntactic, and semantic uncertainties. This results in incorrect pattern detection and analysis. Researchers are employing various text mining techniques that can aid in Topic Modeling, the detection of Trending Topics, the identification of Hate Speeches, and the growth of communities in online social media networks. Objective: This review paper compares the performance of ten machine learning classification techniques on a Twitter data set for analyzing users' sentiments on posts related to airline usage. Methods: Review and comparative analysis of Gaussian Naive Bayes, Random Forest, Multinomial Naive Bayes, Multinomial Naive Bayes with Bagging, Adaptive Boosting (AdaBoost), Optimized AdaBoost, Support Vector Machine (SVM), Optimized SVM, Logistic Regression, and Long-Short Term Memory (LSTM) for sentiment analysis. Results: The results of the experimental study showed that the Optimized SVM performed better than the other classifiers, with a training accuracy of 99.73% and testing accuracy of 89.74% compared to other models. Conclusion: Optimized SVM uses the RBF kernel function and nonlinear hyperplanes to split the dataset into classes, correctly classifying the dataset into distinct polarity. This, together with Feature Engineering utilizing Forward Trigrams and Weighted TF-IDF, has improved Optimized SVM classifier performance regarding train and test accuracy. Therefore, the train and test accuracy of Optimized SVM are 99.73% and 89.74% respectively. When compared to Random Forest, a marginal of 0.09% and 1.73% performance enhancement is observed in terms of train and test accuracy and 1.29% (train accuracy) and 3.63% (test accuracy) of improved performance when compared with LSTM. Likewise, Optimized SVM, gave more than 10% of enhanced performance in terms of train accuracy when compared with Gaussian Naïve Bayes, Multinomial Naïve Bayes, Multinomial Naïve Bayes with Bagging, Logistic Regression and a similar enhancement is observed with AdaBoost and Optimized AdaBoost which are ensemble models during the experimental process. Optimized SVM also has outperformed all the classification models in terms of AUC-ROC train and test scores.

Identifying Crisis Response Communities in Online Social Networks for Compound Disasters: The Case of Hurricane Laura and COVID-19

Online social networks allow different agencies and the public to interact and share the underlying risks and protective actions during major disasters. This study revealed such crisis communication patterns during Hurricane Laura compounded by the COVID-19 pandemic. Hurricane Laura was one of the strongest (Category 4) hurricanes on record to make landfall in Cameron, Louisiana, U.S. Using an application programming interface (API), this study utilizes large-scale social media data obtained from Twitter through the recently released academic track that provides complete and unbiased observations. The data captured publicly available tweets shared by active Twitter users from the vulnerable areas threatened by Hurricane Laura. Online social networks were based on Twitter’s user influence feature (i.e., mentions or tags) that allows notification of other users while posting a tweet. Using network science theories and advanced community detection algorithms, the study split these networks into 21 components of various size, the largest of which contained eight well-defined communities. Several natural language processing techniques (i.e., word clouds, bigrams, topic modeling) were applied to the tweets shared by the users in these communities to observe their risk-taking or risk-averse behavior during a major compounding crisis. Social media accounts of local news media, radio, universities, and popular sports pages were among those which heavily involved and closely interacted with local residents. In contrast, emergency management and planning units in the area engaged less with the public. The findings of this study provide novel insights into the design of efficient social media communication guidelines to respond better in future disasters.

Understanding antecedents of active product recommendations behaviour in online social networking communities

The intention to promote voluntary product recommendation by individuals in the community during online social contact has aroused more and more interest. At present, in a network community composed of consumers, the reasons for personal active product recommendation by consumers are not obvious. In order to further explore the personal active product recommendation behaviour in the community during online social contact and the key factors that influence this behaviour, we have developed a model that, based on the Cognitive Emotion Theory, examines the reciprocal relationships in the community environment and the emotional impact of community homogeneity on the sense of community belonging and community receptivity, which ultimately influences the subsequent active product recommendation behaviour of individuals. The SEM-PLS technique was used to analyse the data collected from a questionnaire survey of 480 WeChat community users. The results of the study show that good community reciprocity and homogeneity have a positive impact on a sense of community belonging and receptivity, and these factors in turn have a positive impact on active product recommendation behaviour. The implications of these findings for both theory and practice are discussed, including the importance of fostering positive reciprocity and homogeneity in online communities to increase individuals’ active product recommendation behaviour. The findings provide insights for online community administrators on how to foster a sense of community belonging and receptivity, which can ultimately lead to a more active and engaged community.

Examining the Role of Perceived Social Benefit and Trust Matter in Sustainability of Online Social Networking Community - Social Exchange Theory Perspective

Examining the Role of Perceived Social Benefit and Trust Matter in Sustainability of Online Social Networking Community - Social Exchange Theory Perspective

Examining the role of perceived social benefit and trust matter in sustainability of online social networking community - social exchange theory perspective

Examining the role of perceived social benefit and trust matter in sustainability of online social networking community - social exchange theory perspective

Social Opinion Network Analytics in Community Based Customer Churn Prediction

Community based churn prediction, or the assignment of recognising the influence of a customer’s community in churn prediction has become an important concern for firms in many different industries. While churn prediction until recent times have focused only on transactional dataset (targeted approach), the untargeted approach through product advisement, digital marketing and expressions in customer’s opinion on the social media like Twitter, have not been fully harnessed. Although this data source has become an important influencing factor with lasting impact on churn management. Since Social Network Analysis (SNA) has become a blended approach for churn prediction and management in modern era, customers residing online predominantly and collectively decide and determines the momentum of churn prediction, retention and decision support. In existing SNA approaches, customers are classified as churner or non-churner (1 or 0). Oftentimes, the customer’s opinion is also neglected and the network structure of community members are not exploited. Consequently, the pattern and influential abilities of customers’ opinion on relative members of the community are not analysed. Thus, the research developed a Churn Service Information Graph (CSIG) to define a quadruple churn category (churner, potential churner, inertia customer, premium customer) for non-opinionated customers via the power of relative affinity around opinionated customers on a direct node to node SNA. The essence is to use data mining technique to investigate the patterns of opinion between people in a network or group. Consequently, every member of the online social network community is dynamically classified into a churn category for an improved targeted customer acquisition, retention and/or decision supports in churn management.

On the Behaviour of Deviant Communities in Online Social Networks

On-line social networks are complex ensembles of inter-linked communities that interact on different topics. Some communities are characterized by what are usually referred to as deviant behaviours, conducts that are commonly considered inappropriate with respect to the society’s norms or moral standards. Eating disorders, drug use, and adult content consumption are just a few examples. We refer to such communities as deviant networks. It is commonly believed that such deviant networks are niche, isolated social groups, whose activity is well separated from the mainstream social media life. According to this assumption, research studies have mostly considered them in isolation. In this work we focused on adult content consumption networks, which are present in many on-line social media and in the Web in general. We found that few small and densely connected communities are responsible for most of the content production. Differently from previous work, we studied how such communities interact with the whole social network. We found that the produced content flows to the rest of the network mostly directly or through bridge-communities, reaching at least 450 times more users.We also show that a large fraction of the users can be inadvertently exposed to such content through indirect content resharing. We also discuss a demographic analysis of the producers and consumers networks. Finally, we show that it is easily possible to identify a few core users to radically uproot the diffusion process. We aim at setting the basis to study deviant communities in context.

Measuring the community value in online social networks

Communities in social networks form with different purposes and play a significant role in interpersonal interactions. Analysis of virtual communities indicates a more precise understanding of the behaviours and desires of individuals in social networks. In this paper, new measures have been proposed for analyzing implicit and explicit communities in Online Social Networks (OSNs). The measures of “potential value of the community members” and “value of the community messages”, which are used for calculating the measure of “community value” are among the most important measures introduced in this paper. Another measure introduced is “user influence rate” in a community, which represents the contribution of a person in creating value in a community. To provide a sound dataset, we collected the information from several real implicit communities in Twitter based on different hashtags. Finally, the suggested measures have been analyzed and compared statistically and behaviourally across different communities. The results of this research well indicate the importance and practicality of the measures introduced in Community analysis of Twitter.

Exploiting homophily to characterize communities in online social networks

SummaryOnline social networks (OSNs) have become one of the most popular platforms where people communicate by sharing contents and personal information. The interactions performed by the users allow to identify the homophily between users and reveal the presence of several communities that could depend on several factors: such as the type of relationships (eg, colleagues and school mates) or to users' preferences (eg, users' interests or hobbies). A very important issue in this scenario is the necessary to characterize such communities by using known real properties or attributes about their members. In this article, we propose an approach that identifies the communities of users by exploiting several community detection algorithms. Afterward, for each user, we exploit decision trees to find a model that describes and distinguishes community affiliations based on known attributes of the members. The evaluation of our approach is derived from a real dataset which consists of the profile information, relationships, and interactions of 95 716 Facebook users. The experimental results show that the proposed approach is able to correctly recognize which attributes of the members properly characterize their corresponding community while ensuring a high level of accuracy (about 85%).

Social Group Recommendation With TrAdaBoost

In recent years, group recommendation has become a research hotspot and focus in online social network community. Currently, several deep-learning-based approaches are leveraged to learn preferences of groups for items and predict the next items in which groups may be interested. Yet, their recommendation performance is still unsatisfactory due to the sparse group-item interactions. In order to address this problem, in this article, we introduce an effective model, namely Social Group Recommendation model with TrAdaBoost (SGRTAB), to raise the performance of group recommendation in online social networks. The SGRTAB model includes two stages: data preprocessing (DP) and model optimization (MO). In DP, SGRTAB produces inputs for MO and implements three related tasks: extracting individual features, handling group data via GloVe, and utilizing user contribute ratings to their own groups, whereas in MO, SGRTAB implements group preference learning with the assistance of user preference learning based on the TrAdaBoost algorithm. Specifically, SGRTAB can effectively absorb the knowledge of user preferences into the process of group preference learning through the idea of transferring-ensemble learning. Moreover, extensive experiments on four real-world data sets indicate that the proposed SGRTAB model significantly outperforms the state-of-the-art baselines for social group recommendation.

A Temporal User Attribute-Based Algorithm to Detect Communities in Online Social Networks

The world is witnessing the daily emergence of a vast variety of online social networks and community detection problem is a major research area in online social network studies. The existing community detection algorithms are mostly edge-based and are evaluated using the modularity metric benchmarks. However, these algorithms have two inherent limitations. Firstly, they are based on a pure mathematical object which considers the number of connections in each community as the main measures. Consequently, a resolution limit and low accuracy in finding community members in often observed. Whereas, online social networks are dynamic networks and the key players are humans whose main attributes such as lifespan, geo-location, the density of interactions, and user weight, change over time. These attributes tend to influence the formation of user communities in any category of online social network. Secondly, the output structure of existing community detection algorithms is usually provided as a graph and dendrogram. A graph structure, is, however, characterized by a high memory complexity, and subsequently exponential search time complexity. Implementing dendrogram such a complex structure is complicated. To address memory complexity and the accuracy rate of the community detection issues, this paper proposes a new temporal user attribute-based algorithm, namely the recently largest interaction based on the attributes of a typical online social network user. Experimental results show that the proposed algorithm outperforms eight well-known algorithms in this domain.

User interaction-oriented community detection based on cascading analysis

Detecting user communities in online social networks (OSNs) is of great importance for understanding social dynamics. Compared to user connections, user interactions have been shown to be more meaningful for reflecting peer relationship in OSNs. To this end, we propose a user interaction-oriented community detection method based on cascading analysis. Specifically, user interactions are analyzed from a large collection of social object sharings (e.g., blog posts, photo shares). Both direct and indirect user interactions associated with each social object sharing are then extracted and the cascading relations among these interactions are captured using a graph representation. The proposed method makes use of such cascading relations to extract groups of actively interacting users and adopts a super graph approach to cluster these user groups for detecting communities. An extensive evaluation of our method was performed using three real OSN datasets and compared with three state-of-the-art overlapping community detection methods, namely two general methods applied to the interaction graph and an interaction-based method. Our method outperformed the compared methods, as demonstrated by several evaluation metrics, and produced more robust and stable detection results across different datasets.

Creating Community Online

Student usage of online social networks (OSNs) has become prominent in the lives and experiences of today's college students. While not every student may be actively using OSNs, everybody knows about them and most are avid users. Despite the potential drawbacks focused on in the news media, students continue to use these online communities. The overarching research questionfor this study is: What are the effects of online social networking communities on the experiences of college students? In looking at this phenomenon from the students' perspective, it is apparent that students have a comprehensive understanding of drawbacks with regards to OSNs. Most students believe that limited interaction with and presence of their universities within OSNs would be acceptable or tolerable. In many cases, students welcomed the university presence as an opportunity for universities to be more aware of the student culture on campus and for universities to educate them about how to be safe and smart in their online activities. By understanding students' perceptions of the uses of OSNs, the benefits and drawbacks of OSNs, and the level of involvement that university staff and administrators should have within OSNs, student affairs practitioners can be more supportive of student participation in these networks, and through that, enhanve student engagement with the university.

Artificial Neural Network Research in Online Social Networks

Artificial neural networks are a machine learning method ideal for solving classification and prediction problems using Big Data. Online social networks and virtual communities provide a plethora of data. Artificial neural networks have been used to determine the emotional meaning of virtual community posts, determine age and sex of users, classify types of messages, and make recommendations for additional content. This article reviews and examines the utilization of artificial neural networks in online social network and virtual community research. An artificial neural network to predict the maintenance of online social network “friends” is developed to demonstrate the applicability of artificial neural networks for virtual community research.

Description, characterization, and evaluation of an online social networking community: the American Cancer Society's Cancer Survivors Network®.

To describe (a) the conceptualization, purpose, and features of The American Cancer Society's Cancer Survivors Network® (CSN; http://csn.cancer.org ), (b) the ongoing two-phase evaluation process of CSN, and (c) the characteristics of CSN members. An online opt-in self-report survey of CSN members (N = 4762) was conducted and digital metrics of site use were collected. Annually, CSN attracts over 3.6 million unique users from over 200 countries/territories. Most commonly used site features are discussion boards (81.1%), the search function (63.8%), and the member resource library (50.2%). The survey sample is mostly female (69.6%), non-Hispanic white (84.1%), and self-identified as a cancer survivor (49.8%), or both cancer survivor and cancer caregiver (31.9%). A larger number of survey respondents reported head and neck cancer (12.5%), relative to cancer incidence/prevalence data. The volume of CSN traffic suggests high demand among cancer survivors and caregivers for informational and/or emotional support from other cancer survivors and caregivers. CSN may be particularly beneficial for individuals with rare cancers. Furthermore, this study documents a group of individuals whose cancer experience is multifaceted (e.g., survivors became caregivers or vice versa), and for whom CSN has the capacity to provide support at multiple points during their cancer experiences. CSN is a free, internet-based social networking site available to all cancer survivors and caregivers, worldwide. Evaluation of the site is ongoing and will be used to inform improvements to usability, reach, recruitment, retention, and potential health impact(s) of this valuable resource.

Product recommendation in online social networking communities: An empirical study of antecedents and a mediator

Intention to facilitate individual voluntary product recommendation in online social networking communities has attracted increased attention. However, it is not obvious why individuals recommend products in a community composed of non-transactional users. What are the critical factors influencing such a behavior? To attempt to provide insights into the product recommendation behavior in online social networking communities, we develop a model to investigate the effects of community atmospheric cues on affective social distance and subsequent product recommendations based on the stimulus–organism–response paradigm. By analyzing the results of a survey using a questionnaire, we found that community reciprocity and community receptivity have a direct influence on product recommendation, while community atmospheric cues have an indirect influence through affective social distance. Theoretical and managerial implications are discussed.

A Comparison of Intercultural Student Communities in Online Social Networks

Abstract This work is geared to analyze informal learning processes in student conferences. In particular, it compares social network interactions occurring in conference-related Facebook pages - i. e. Taiwan-America Student Conference (TASC), Japan-America Student Conference (JASC) and Korea-America Student Conference (KASC) pages - within a period starting 30 days before and finishing 30 days after the application deadlines. This empirical study has been realized by adopting open source visualization tools and techniques freely available on the software market in order to perform Social Network Analysis (SNA) in a transparent and reproducible way. Such an analysis provides interesting information on interaction dynamics, emerging hot topics and sub-group formation of attending students.

Behavior-based indices for evaluating communities in online social networks

Behavior-based indices for evaluating communities in online social networks

Editorial

Editorial

Intercultural communication in online social networking discourse

ABSTRACTThis article presents a case study that examines how an online social networking community is constituted through intercultural discourse on the part of one learner sojourning in the US. Using Byram’s model of intercultural communicative competence, this study examines the learner’s naturalistic communication in a social networking site (SNS). The qualitative analysis indicates that the learner acted upon the affordances of SNSs to reflect on her intercultural experiences and displayed evidence of intercultural understanding during her study abroad years. The learner’s intercultural knowledge, attitudes, and discovery are shown to be co-constructed by her and other members in the online discourse. This study indicates that computer-mediated communities like SNSs provide the learner with a space in which she documented her cultural experience and reflected on those experiences in real-time exchanges with multilingual, multicultural users. Pedagogical implications are discussed.