Network formed between users in a social media can be used to encourage information spreading among them. This research applied Social Network Analysis which further can be used to social media marketing to improve the marketing process effectively. Based on previous research, information spreading speed among the social media is affected by the users' activity connection which can be represented in centrality value. The centrality value itself is very affected by the graph structure and weights. This research applied degree and eigenvector centrality to observe the effect of centrality value for twitter data. The result shows that there is significant difference among 10 most influential users. This result will be used for the future research that will be focused in small and medium enterprise (SME) twitter data.

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In this paper we study how to determine the nodes that most influential to a node in the network. Social Network Analysis (SNA) can measure the centrality of a node in order to obtain an influential nodes in the dissemination of information. One of the centrality measurement that can be applied is degree centrality. In this research, the method used is Opsahl method, combines two indicators, the number of neighborhood (degree) and the amount of weight relations (strength) of a node and uses tuning parameters. The weight relations are obtained from the number of relations as following/follower, mention and reply. Tuning parameters are parameters which are used to set the influence of both degree and strength to the degree centrality measurement results. Based on test results, the node who has a high strength value is derived from weight relations which are obtained from mentions and replies.

<span lang="EN-US">The popularity of social media has drawn the attention of researchers who have conducted cross-disciplinary studies examining the relationship between personality traits and behavior on social media. Most current work focuses on personality prediction analysis of English texts, but Indonesian has received scant attention. Therefore, this research aims to predict user’s personalities based on Indonesian text from social media using machine learning techniques. This paper evaluates several machine learning techniques, including <a name="_Hlk87278444"></a>naive Bayes (NB), K-nearest neighbors (KNN), and support vector machine (SVM), based on semantic features including emotion, sentiment, and publicly available Twitter profile. We predict the personality based on the big five personality model, the most appropriate model for predicting user personality in social media. We examine the relationships between the semantic features and the Big Five personality dimensions. The experimental results indicate that the Big Five personality exhibit distinct emotional, sentimental, and social characteristics and that SVM outperformed NB and KNN for Indonesian. In addition, we observe several terms in Indonesian that specifically refer to each personality type, each of which has distinct emotional, sentimental, and social features.</span>

Social Network Analysis (SNA) has been applied in several case studies. SNA is applied to enhance the company's marketing strategy as well as small and medium businesses. This research proposes a collaborative model using content-based and userbased approach, with the centrality measurement methods. Content-based approach tends to focus on tweet content analysis of the existing nodes in a network, while the user-based approach focuses on the connections between users in the network twitter.The model will combine the advantages of collaborative content-based and user-based approach, to find the most influential people in a twitter network to make the dissemination of information more effectively and efficiently.