Collecting Data of SNS User Behavior to Detect Symptoms of Excessive Usage: Development of Data Collection Application

Intapong, Ploypailin; Achalakul, Tiranee; Ohkura, Michiko

doi:10.1007/978-3-319-41685-4_9

Cited by 7 publications

(7 citation statements)

References 14 publications

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“…Abdesslem et al [16] believe that "more reliable data can be obtained by using a new methodology based on the combinations of existing methods: this way, the data collected come from different sources and describe better users' behaviors." Therefore, we employed self-reported and SNS measurement approaches and designed and developed a web-based application as a tool for aggregating data from questionnaires and SNSs by APIs [2,3]. We analyzed the obtained data to clarify the characteristics of SNS usage and relationships with SNS addiction.…”

Section: ) Application Deploymentmentioning

confidence: 99%

“…We designed a data collection application and developed a tool for collecting SNS data from questionnaires and SNSs by APIs [2,3]. Its architecture design is shown in Figure 1.…”

Section: Data Collection Applicationmentioning

confidence: 99%

“…Its architecture design is shown in Figure 1. The methods for retrieving the data were previously described [3,20,21].…”

Section: Data Collection Applicationmentioning

confidence: 99%

“…Our questionnaire, which was designed for gathering user experiences with SNSs, has three parts: personal information, SNS usage, and SNS addiction [2]. In the third part, we employed IAT [7] and BFAS [6], both of which we modified for measuring SNS addiction by retaining the original concepts.…”

Section: Questionnairementioning

confidence: 99%

“…We designed and developed a data collection application as a tool for collecting SNS data from questionnaires and SNSs by APIs from 177 volunteers at the Thai-Nichi Institute of Technology (TNI), Thailand [2,3]. This paper introduces the results of our data analysis to explore the factors that correlate with SNS addiction among undergraduates in Thailand.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Analysis of Questionnaires on User Behavior for Assessing Symptoms of Excessive SNS Usage

Intapong

Charoenpit

Achalakul

et al. 2018

IJAE

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LITERATURE SURVEY Negative consequences of excessive SNS usageMany studies have argued that excessive SNS usage causes various negative consequences. People who spend too much time on SNSs are less involved in their real life communities [1]. Students who use Facebook have lower grades than those who do not because the former are easily distracted and exercise poor time management [1]. J. concluded that Kuwaiti university students who spend time on SNSs instead of studying have lower grades. In Thailand, many teenagers suffer from such negative effects of excessive SNS usage as lack of sleep, reduced academic performance, inappropriate manners, negative emotional expressions, impairment of family and social functions, and mental health problems [5]. Excessive SNS usage delays bedtimes and reduces sleep quality [6].Regarding those research outcomes, excessive SNS usage causes relational, performance, health-related, and emotional problems, including the risk of addiction [1]. SNS addictionThe excessive and compulsive use of SNSs has been linked to behavioral addictions [6]. SNS addiction also shares similarities with other behavioral addictions [1,6] Technology, Tokyo, Japan Abstract: The use of social networking sites (SNSs) continues to dramatically increase. People are spending unexpected and unprecedented amounts of time online. Such often excessive and compulsive use has been categorized as a behavioral addiction. This research is conducted to assess the symptoms of excessive SNS usage by studying user behavior in SNSs. We designed and developed a data collection application as a tool for gathering data from questionnaires and SNSs by APIs from 177 volunteers at the Thai-Nichi Institute of Technology (TNI), Thailand. This paper introduces the analysis results of questionnaire data to find the factors associated with SNS addiction. Our analytic results identified potential candidates for the key components of SNS addiction.

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Section: ) Application Deploymentmentioning

confidence: 99%

“…We designed a data collection application and developed a tool for collecting SNS data from questionnaires and SNSs by APIs [2,3]. Its architecture design is shown in Figure 1.…”

Section: Data Collection Applicationmentioning

confidence: 99%

“…Its architecture design is shown in Figure 1. The methods for retrieving the data were previously described [3,20,21].…”

Section: Data Collection Applicationmentioning

confidence: 99%