Content Modeling Based on Social Network Community Activity

Abstract

The advancement of knowledge society has enabled the social network community (SNC) to be perceived as another space for learning where individuals produce, share, and apply content in self-directed ways. The content generated within social networks provides information of value for the participants in real time. Thus, this study proposes the social network community activity-based content model (SoACo Model), which takes SNC-based activities and embodies them within learning objects. The SoACo Model consists of content objects, aggregation levels, and information models. Content objects are composed of relationship-building elements, including real-time, changeable activities such as making friends, and participation-activity elements such as "Liking" specific content. Aggregation levels apply one of three granularity levels considering the reusability of elements: activity assets, real-time, changeable learning objects, and content. The SoACo Model is meaningful because it transforms SNC-based activities into learning objects for learning and teaching activities and applies to learning management systems since they organize activities -such as tweets from Twitter --depending on the teacher's intention.

Social group recommendation based on dynamic profiles and collaborative filtering

“…Dynamic profiles are extracted through group activity details for users who have activity groups already, and groups joined by a user are searched and classified by category (Lines 6-8). Once groups are classified, users with similar preferences within a group are searched through Equation (3). After this, the top n users with similar preferences are selected as experts, while a score is assigned as keywords that are found in users with similar preferences are identified (Lines 9-16).…”

“…As users' demand for social networking and information sharing has increased more and more, the use of social network has also risen [1,2,3,4,28,29,30]. As a result, systems that recommend content and products that are likely to be preferred by users are widely employed, and various personalized services utilizing such recommendation systems have been provided [18,19,20].…”

k-Cliques mining in dynamic social networks based on triadic formal concept analysis

“…Clique as a very common structure in social networks, which is composed of the set of vertices as well as the reciprocal relationships among them, reflects the social behavior and its social features among users. Therefore, clique detection is playing an important role in various applications, such as social recommendation [2], network routing [3] , community detection [6], content modeling [7], and finding the frequently occurring patterns in protein structures [8]. For instance, in an online social learning network, finding the collaborative teams with the required number of learners for completing some given assignments is one of cases of clique detection.…”

Getting Business Insights through Clustering Online Behaviors

“…Most of the studies on the Internet and the users have tried to understand who they are and what they are doing online [4][5][6]. These studies conducted segmentation and grouped the online users by such variables as age, gender, and lifestyle [7][8][9]. The studies have used mostly survey data, which indicates what online users perceived they did online.…”