Modeling and detecting change in user behavior through his social media posting using cluster analysis

2017 ◽  
Author(s):  
Deepali J. Joshi ◽  
Nikhil Supekar ◽  
Rashi Chauhan ◽  
Manasi S. Patwardhan
Keyword(s):  
Social Media ◽  
Cluster Analysis ◽  
User Behavior

Predictability of User Behavior in Social Media: Bottom-Up v Top-Down Modeling

2013 ◽  
Author(s):  
David Darmon ◽  
Jared Sylvester ◽  
Michelle Girvan ◽  
William M. Rand
Keyword(s):  
Social Media ◽  
User Behavior ◽  
Top Down ◽  
Bottom Up

An Experimental Study of Spammer Detection on Chinese Microblogs

2020 ◽  
Vol 30 (11n12) ◽  
pp. 1759-1777
Author(s):  
Jialing Liang ◽  
Peiquan Jin ◽  
Lin Mu ◽  
Jie Zhao
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
User Behavior ◽  
Real Data ◽  
User Profile ◽  
Data Set ◽  
Sina Weibo ◽  
Factors Affecting ◽  
The Government ◽  
Hot Event

With the development of Web 2.0, social media such as Twitter and Sina Weibo have become an essential platform for disseminating hot events. Simultaneously, due to the free policy of microblogging services, users can post user-generated content freely on microblogging platforms. Accordingly, more and more hot events on microblogging platforms have been labeled as spammers. Spammers will not only hurt the healthy development of social media but also introduce many economic and social problems. Therefore, the government and enterprises must distinguish whether a hot event on microblogging platforms is a spammer or is a naturally-developing event. In this paper, we focus on the hot event list on Sina Weibo and collect the relevant microblogs of each hot event to study the detecting methods of spammers. Notably, we develop an integral feature set consisting of user profile, user behavior, and user relationships to reflect various factors affecting the detection of spammers. Then, we employ typical machine learning methods to conduct extensive experiments on detecting spammers. We use a real data set crawled from the most prominent Chinese microblogging platform, Sina Weibo, and evaluate the performance of 10 machine learning models with five sampling methods. The results in terms of various metrics show that the Random Forest model and the over-sampling method achieve the best accuracy in detecting spammers and non-spammers.

scholarly journals Computational landscape of user behavior on social media

2018 ◽  
Vol 98 (6) ◽  
Author(s):  
David Darmon ◽  
William Rand ◽  
Michelle Girvan
Keyword(s):  
Social Media ◽  
User Behavior

scholarly journals Discontinuous Usage Intention of Millennials in Using Facebook: The Role of SNS Exhaustion Mediates Social Overload

2019 ◽  
Vol 4 (3) ◽  
Author(s):  
Putu Laksmita Dewi Rahmanyanti ◽  
Ni Nyoman Kerti Yasa
Keyword(s):  
Social Media ◽  
Social Networking Sites ◽  
Sampling Method ◽  
User Behavior ◽  
Negative Results ◽  
Analysis Technique ◽  
The Social ◽  
Data Collection Technique ◽  
Continuous Use

Social Networking Sites (SNS) or commonly called social media is an online service that aims to build social relationships with users who share their interests and activities. Percentage of Facebook users decreased during 2018 from January 2018 which was initially 75.5% to 66.3% in December 2018. Users complain about the excessive influence of SNS on their lives and react in various forms of behavior to stop using services. Non-continuous use as a user behavior towards stress due to SNS fatigue and social overload. The sample used is 60 respondents which are determined using the purposive sampling method. The data collection technique utilized is by questionnaires with the Likert scale measurement method, while the data are analyzed using the path analysis technique. The research results show that the social overload on SNS exhaustion and SNS exhaustion variable on discontinuous usage intention have a positive and significant influence but social overload on discontinuous usage intention have a positive but non significant effect. Likewise, SNS exhaustion is able to mediate the influence of social overload on discontinuous usage intention. Users can actively control their behavior to avoid potential negative results caused by social overload. Social media providers must effectively prevent the emergence of negative emotions from users by providing a system that gives users to manage whatever information they can receive and share only with a few people so that users do not receive information about their friends on Facebook excessively.

scholarly journals Concerns Expressed by Chinese Social Media Users During the COVID-19 Pandemic: Content Analysis of Sina Weibo Microblogging Data (Preprint)

2020 ◽  
Author(s):  
Junze Wang ◽  
Ying Zhou ◽  
Wei Zhang ◽  
Richard Evans ◽  
Chengyan Zhu
Keyword(s):  
Social Media ◽  
Latent Dirichlet Allocation ◽  
Vaccine Development ◽  
User Behavior ◽  
Chinese Government ◽  
Web Crawler ◽  
Health Crisis ◽  
User Behavior Analysis ◽  
Sina Weibo ◽  
Social Media Platforms

BACKGROUND The COVID-19 pandemic has created a global health crisis that is affecting economies and societies worldwide. During times of uncertainty and unexpected change, people have turned to social media platforms as communication tools and primary information sources. Platforms such as Twitter and Sina Weibo have allowed communities to share discussion and emotional support; they also play important roles for individuals, governments, and organizations in exchanging information and expressing opinions. However, research that studies the main concerns expressed by social media users during the pandemic is limited. OBJECTIVE The aim of this study was to examine the main concerns raised and discussed by citizens on Sina Weibo, the largest social media platform in China, during the COVID-19 pandemic. METHODS We used a web crawler tool and a set of predefined search terms (<i>New Coronavirus Pneumonia</i>, <i>New Coronavirus</i>, and <i>COVID-19</i>) to investigate concerns raised by Sina Weibo users. Textual information and metadata (number of likes, comments, retweets, publishing time, and publishing location) of microblog posts published between December 1, 2019, and July 32, 2020, were collected. After segmenting the words of the collected text, we used a topic modeling technique, latent Dirichlet allocation (LDA), to identify the most common topics posted by users. We analyzed the emotional tendencies of the topics, calculated the proportional distribution of the topics, performed user behavior analysis on the topics using data collected from the number of likes, comments, and retweets, and studied the changes in user concerns and differences in participation between citizens living in different regions of mainland China. RESULTS Based on the 203,191 eligible microblog posts collected, we identified 17 topics and grouped them into 8 themes. These topics were pandemic statistics, domestic epidemic, epidemics in other countries worldwide, COVID-19 treatments, medical resources, economic shock, quarantine and investigation, patients’ outcry for help, work and production resumption, psychological influence, joint prevention and control, material donation, epidemics in neighboring countries, vaccine development, fueling and saluting antiepidemic action, detection, and study resumption. The mean sentiment was positive for 11 topics and negative for 6 topics. The topic with the highest mean of retweets was domestic epidemic, while the topic with the highest mean of likes was quarantine and investigation. CONCLUSIONS Concerns expressed by social media users are highly correlated with the evolution of the global pandemic. During the COVID-19 pandemic, social media has provided a platform for Chinese government departments and organizations to better understand public concerns and demands. Similarly, social media has provided channels to disseminate information about epidemic prevention and has influenced public attitudes and behaviors. Government departments, especially those related to health, can create appropriate policies in a timely manner through monitoring social media platforms to guide public opinion and behavior during epidemics.

Supporting Policy-Makers with Social Media Analysis Tools to Get Aware of Citizens' Opinions

2014 ◽  
pp. 121-156
Author(s):  
Timo Wandhöfer ◽  
Steve Taylor ◽  
Miriam Fernandez ◽  
Beccy Allen ◽  
Harith Alani ◽  
...  
Keyword(s):  
Social Media ◽  
Information Overload ◽  
User Behavior ◽  
Relevant Information ◽  
Media Analysis ◽  
Control Group ◽  
Data Sets ◽  
Online Forums ◽  
Policy Makers ◽  
Facebook Pages

The role of social media in politics has increased considerably. A particular challenge is how to deal with the deluge of information generated on social media: it is impractical to read lots of messages with the hope of finding useful information. In this chapter, the authors suggest an alternative approach: utilizing analysis software to extract the most relevant information of the discussions taking place. This chapter discusses the WeGov Toolbox as one concept for policy-makers to deal with the information overload on Social Media, and how it may be applied. Two complementary, in depth case studies were carried out to validate the usefulness of the analysis results of the WeGov Toolbox components' within its target audience's everyday life. Firstly, the authors used the “HeadsUp” forum, operated by the Hansard Society. Here, they were able to compare the key themes and opinions extracted automatically by the Toolbox to a control group of manually pre-analyzed data sets. In parallel, results of analyses based on four weeks' intensive monitoring on policy area-specific Facebook pages selected by German policy makers, as well as topics on Twitter globally and local, were assessed by taking into account their existing experience with content discussed and user behavior in their respective public spheres. The cases show that there are interesting applications for policy-makers to use the Toolbox in combination with online forums (blogs) and social networks, if behavioral user patterns will be considered and the framework will be refined.

Social Media Activities

2014 ◽  
pp. 88-103
Author(s):  
Kristina Heinonen
Keyword(s):  
Social Media ◽  
Social Networking ◽  
Conceptual Framework ◽  
Social Networking Sites ◽  
User Behavior ◽  
Social Connection ◽  
Message Boards ◽  
Online Ratings ◽  
Online Message Boards ◽  
Marketing Activities

Consumers are increasingly consuming, participating, contributing, and sharing different types of online content. This is influencing the marketing activities traditionally controlled and performed by companies. The aim of this chapter is to conceptualize the activities consumers perform in social media. Social media denote content created by individual consumers such as online ratings or verbal reviews, online message boards/forums, photos/video sites, blogs, tags, and social networking sites. A conceptual framework for consumers' social media activities is developed and qualitatively substantiated. Social media activities are based on the motives for the activities, including information, social connection, and entertainment. The chapter contributes to research on social media and online communities by describing user behavior and motivations related to the user-created services. Managerially, the study deepens the understanding of different challenges related to users' activities on social media and the motivations associated with those activities.

Deepening the Understanding of Students´ Study-Related Media Usage

2018 ◽  
Vol 8 (3) ◽  
pp. 45-59
Author(s):  
Joachim Stöter
Keyword(s):  
Social Media ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Instructional Design ◽  
Design Process ◽  
Qualitative Interviews ◽  
Media Usage ◽  
Short Questionnaire ◽  
Instructional Design Process ◽  
Usage Patterns

This article describes how numerous studies on student usage of various digital applications, social media and networks are available but studies on study-related media usage typologies are rare. Based on the instruments developed by Zawacki-Richter, Müskens, Krause, Alturki, and Aldraiweesh, as well as Zawacki-Richter, Kramer and Müskens, a short questionnaire was developed and tested with a cohort of 72 students. The results of the factor analysis suggest statistically relevant scales, which are suitable for classifying students along their media usage patterns through a subsequent cluster analysis. The three clusters that were determined can be compared with the usage types from Zawacki-Richter et al. During the instructional design process these heterogeneous groups and their media usage should be taken into consideration. The identified items can be applied in order to develop qualitative interviews for a deeper understanding of the usage types.

A Method of Interest Degree Mining Based on Behavior Data Analysis

2019 ◽  
Vol 34 (09) ◽  
pp. 2059030
Author(s):  
Zhen Li ◽  
Shuo Xu ◽  
Tianyu Wang
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Fusion ◽  
Language Processing ◽  
User Behavior ◽  
Comprehensive Evaluation ◽  
Mapping Method ◽  
User Preference ◽  
User Interest ◽  
Improvement Measures

Based on big data, this paper starts from the behavior data of users on social media, and studies and explores the core issues of user modeling under personalized services. Focusing on the goal of user interest modeling, this paper proposes corresponding improvement measures for the existing interest model, which has great difference in interest description among different users and it is difficult to find the user interest change in time. For the above problems, this paper takes user-generated content and user behavior information as the analysis object, and uses natural language processing, knowledge warehouse, data fusion and other methods and techniques to numerically analyze user interest mining based on text mining and multi-source data fusion. We propose a user interest label space mapping method to avoid data sparse problem caused by too many dimensions in interest analysis. At the same time, we propose a method to extract and blend the long-term and short-term interests, and realize the comprehensive evaluation of interests. In the analysis of the big data phase, the user preference social property application preference value law, it is expected to achieve user Internet social media application preference data mining from the perspective of big data.

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