Abstract
The use of social networking sites (SNSs) continues to dramatically increase. People are spending unexpected and unprecedented amounts of time online. Excessive and compulsive use of them has been categorized as a behavioral addiction. This research is conducted to assess the symptoms of excessive SNS usage by studying user behavior and emotion in SNSs. We designed a data collection application and developed a tool for collecting data from questionnaires and SNSs by APIs. The data were collected at the Thai-Nichi Institute of Technology (TNI), Thailand from 177 volunteers. We introduce our analysis of data obtained by SNS APIs by focusing on Facebook and Twitter. We used modified IAT and BFAS to measure SNS addiction. The Facebook and Twitter results, including a combination with questionnaires, were analyzed to identify the factors associated with SNS addiction. Our analytic results identified potential candidates of the key components of SNS addiction.
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1 Introduction
Digital technology plays an important role in daily life. Social Networking Sites (SNSs) have exploded as a popular type of communication where groups virtually meet and interact with others who share similar interests [1].
In January 2016, a summary of SNS usage from WeAreSocial reported that the total population is about 7.4 billion while about 3.4 billion are active SNS users, which equal almost 30% of the world’s population actively use SNSs [2]. In Thailand, over 50% of population is active SNS users. In addition, a survey of Thai Internet users by ETDA [3] reported that 96% use SNSs. The top three most popular SNSs are YouTube, Facebook, and Line [3]. The average daily SNS use was almost three hours [2]. Unfortunately, some people spend too much time on SNSs and use them in potentially deleterious ways. Many studies [1] have warned about the negative consequences of excessive SNS usage, including the risk of addictive behavior.
This research studied user behavior and emotion related to SNS usage to assess the symptoms of excessive SNS usage. We divided our research into the following four main stages:
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1.
Collect SNS user behavior data
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2.
Clarify the characteristic of SNS usage and their relationships
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3.
Estimate user emotions of SNS usage using biological signals
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4.
Detect symptoms of excessive SNS usage
In the first stage, we previously designed and developed a data collection application as a tool for collecting SNS user behavior data from questionnaires and SNSs by APIs [4, 5]. The questionnaires gathered user experiences with SNSs. Modified IAT and BFAS were employed as part of a questionnaire to measure SNS addiction. APIs were used for directly retrieving data from SNSs. The data were collected at the Thai-Nichi Institute of Technology (TNI), Thailand from 177 volunteers. Figure 1 illustrates the procedures for collecting the SNS data.
The second stage is in progress. The data obtained by the application will be analyzed to clarify the characteristics of SNS usage and their relationships. In a previous study [6], the data obtained by questionnaires were analyzed to find the factors associated with SNS addiction. However, that approach was insufficient for capturing every aspect of the users. Therefore, in this article, we introduce the analysis of data obtained by SNS APIs, including a combination with questionnaire data to improve data analysis for identifying the factors associated with SNS addiction.
The analytic results will be applied for detecting the symptoms of excessive SNS usage and developing prevention strategies for increasing the awareness of excessive SNS usage.
2 SNS Addiction
Many studies have argued that excessive SNS usage can cause various negative consequences such as relational, performance, health-related, and emotional problems, including the risk of addiction. The excessive and compulsive use of SNSs has also been linked to behavioral addictions [7].
Internet addiction is one type of behavioral addiction. Young [8, 9] identified five types of internet addiction: computer addiction, information overload, net compulsion, cyber-sexual addiction, and cyber-relationship addiction. SNS addiction falls in the last category [1]. Since Facebook has become one of the world’s most commonly used Internet sites, addiction to it may be a specific form of Internet addiction [10].
SNS addiction shares similarities with other behavioral addictions [1, 7]. Kuss and Griffiths [1] argued that the symptoms of SNS addiction resemble the symptoms of other addictions. The following are the addiction components from a biopsychosocial perspective [10]:
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(1)
Salience: behavioral, cognitive, and emotion preoccupation
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(2)
Mood modification: engagement that modifies/changes emotional states
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(3)
Tolerance: increased amount of time spent on it
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(4)
Withdrawal: unpleasantness when the use is restricted
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(5)
Conflict: relationship problems with family and friends because of usage
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(6)
Relapse: failure to avoid use
3 Method
This study aims to explore the key components for SNS addiction by clarifying the characteristic of SNS usage and their relationships. We experimentally collected data from undergraduate students in Thailand to determine SNS usage variables and the relationships between them. The data were collected at the Thai-Nichi Institute of Technology (TNI).
3.1 Data Collection Application
We designed a data collection application and developed a tool for collecting SNS data from questionnaires and SNSs by APIs [4, 5]. Our application can be accessed through a web browser, i.e., Google Chrome. Its architecture design is shown in Fig. 2, and the data obtained by it are shown in Fig. 3.
3.2 Questionnaire
Questionnaire is one part of our data collection application that gathers user SNS experiences [4]. Our questionnaire is divided into three parts: (1) personal information, (2) SNS usage, and (3) SNS addiction. In the third part, we used IAT [11] and BFAS [10] to reflect the addictive behaviors.
Internet Addiction Test.
The internet addiction test (IAT) is a 20-item questionnaire [11] that measures the characteristics and behaviors associated with compulsive Internet use. IAT is scored on a 6-point Likert scale and results in four levels: none, mild, moderate, and severe.
Bergen Facebook Addiction Scale.
The Bergen Facebook addiction scale (BFAS) is a six-item questionnaire developed [10] to assess Facebook addiction in epidemiology studies and clinical trials. It is scored on a 5-point Likert scale and broken down into normal and excessive users.
Our questionnaire was originally implemented in English. IAT and BFAS were modified for SNSs by retaining the original concepts and cut-off scores. In this experiment, we translated the questionnaire into Thai. We used the Thai version of IAT by Weerachatyankul [12] and the Thai version of BFAS (Thai-BFAS) by Phanasathit et al. [13] and modified them for SNS.
3.3 SNS APIs
We directly retrieved SNS data through SNS APIs. Each SNS provides a different API. First, we focused on retrieving data from Facebook and Twitter.
Graph APIs.
Facebook provides Graph APIs [14] for accessing its data based on HTTPs for various purposes. Most requests require an access token, which is “an opaque string that identifies a user, app or page” [14], which is generally obtained in the OAuth authentication process.
REST APIs.
Twitter provides REST APIs for accessing its data. A REST API is designed to take a number of requests to perform a number of tasks and for smaller stream samples. The REST API identifies Twitter applications and users using OAuth authentication, and the response data are in the JSON formation [15].
We implemented Facebook and Twitter quizzes that asked such questions as “How often do you tweet?” as part of our data collection application. When users complete the quizzes, the data are retrieved by APIs [16].
3.4 Session Identification
Session identification categorizes the different activities performed by each user and segments them into individual access sessions. If the activities are not connected to previous activities or there is more than a 30-minute delay (based on previous empirical findings [17]) between the activities, then it is defined as a different session.
To estimate the time spent on SNSs, we used a session identification approach. We organized the data retrieved from Facebook and Twitter as the sequences of activities with action times (Table 1). We segmented the session and calculated the duration between its first and last activities. For user AAAA in Table 1, the two activities were considered the same session (A) with a 14-minute-time difference. For user BBBB, the four activities were considered the same session (B). BBBB’s sessions lasted nine minutes.
With session identification, we calculated the following variables from the Facebook and Twitter data:
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Frequency of use (times/day)
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Time spent (minutes/time)
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Length of use (minutes/day)
3.5 Experimental Procedure
The following are the experimental procedures:
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1.
Instructor introduces an overview of the research and the data collection application.
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2.
Instructor distributes the instruction documents to the participants and explains the experiment’s procedure.
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3.
Participants access the application by a web browser and follow the procedures in the document.
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(a)
Participants complete Twitter quiz and/or Facebook quiz, based on which the account they use.
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(b)
Participants answer the questionnaires.
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(a)
Participants read and accepted the terms of agreement before they did the quizzes and answered the questionnaires.
4 Results
We did our experiment with 177 volunteers who were undergraduate students in the faculty of Information Technology, the Thai-Nichi Institute of Technology. Their ages ranged from 17 to 26 (x̄ = 21.17, SD = 1.64).
4.1 Facebook
Facebook Usage.
99 participants granted us data-access to their Facebook accounts: 65 males and 34 females. The data were retrieved by Graph APIs in a three-month period. Facebook usage is summarized in Table 2.
The average usage frequency was 1.41 times per day, and the average amount of time spent on Facebook was 15.38 min per session and 22.88 min per day. The most common activities on the user feeds were replying (x̄ = 101.08), followed by posting (x̄ = 84.55). The ratio of posting types was 22% for status updates, 47% for photos, 13% for videos, and 18% for links.
Figure 4 shows the usage activities: posting, commenting, and replying. Facebook users did them several times during the 13:00–18:00 (6.06%) and 18:00–24:00 (21.21%) time periods. They also did these activities daily, except for the 12:00–13:00 period. Chi-square analysis results indicated significant differences among each usage period (p < 0.05).
Facebook Usage and SNS Addiction.
We analyzed the data obtained from Facebook (including the combination with data from questionnaires [6]) to find factors associated with SNS addiction. Since the normality test on Facebook variables resulted in negative outcomes, a non-parametric test was used. The IAT and BFAS results from the questionnaires were used for measuring SNS addiction based on the reasonable results from a previous study [6].
According to the definition of the original IAT level, we named participants as excessive users if their scores appeared in each level of Internet addiction (mild, moderate, and severe) and the others as normal users. The original BFAS also classified users this way.
To examine the relationship of SNS addiction with Facebook variables, the Mann-Whitney U Test was employed. As shown in Table 3, the ratio of usage during the 18:00–24:00 period was significantly different for both the IAT level (z = −2.376, p < 0.05) and the BFAS level (z = −1.966, p < 0.05). Moreover, the ratios of posting status updates (z = −2.305, p < 0.05) and videos (z = −1.974, p < 0.05) were significantly different for the IAT level.
To identify how excessive and normal users differ, we applied logistic regression analysis to both the IAT and BFAS results to determine the importance of the effective variants used to distinguish excessive from normal users. The results are shown in Table 4.
For both IAT and BFAS, the following variables distinguish excessive from normal users:
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number of comments and replies in a three-month period
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number of daily activities (posting, commenting, replying)
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the ratio of usage during 18:00–24:00 period
The ratio of posting video was another effective variant for IAT.
4.2 Twitter
Twitter Usage.
36 participants granted us data-access to their Twitter accounts: 19 males and 17 females. The data were retrieved by REST APIs over a three-month period. A summary of the Twitter usage is shown in Table 5.
The average usage frequency was 2.02 times per day, and the average time spent on Twitter was 14.71 min per session and 40.13 min per day. The average number of years using Twitter was 3.47 years. Twitter activities were tweets (x̄ = 258.81), retweets (x̄ = 166.78), and replies (x̄ = 62.28).
According to Fig. 5, Twitter users engaged in daily activities on it during all periods. After midnight was the top period in which 33.33% of Twitter users engaged in daily activities. Chi-square analysis results indicated that the periods between 13:00–18:00 and after midnight were significantly different from other periods (p < 0.05).
Twitter Usage and SNS Addiction.
The data obtained from Twitter (including the combination with the data from questionnaires [6]) were analyzed to find the factors associated with SNS addiction. Since the normality test on Twitter variables resulted in negative outcomes, we used a non-parametric test. The IAT and BFAS results from the questionnaires were used for measuring SNS addiction.
To examine the relationship of SNS addiction with the Twitter variables, we used the Mann-Whitney U test and logistic regression analysis. The analytic results indicated no significant differences between the Twitter variables and IAT. On the contrary, the results from both the Mann-Whiney U test and logistic regression analysis indicated a significant difference between the ratio of usage after midnight and BFAS.
5 Discussion
In this study, over half of our participants were Facebook users, 20% were Twitter users, and 18% used both Facebook and Twitter. Our descriptive analysis results indicated that the Facebook and Twitter usages are different.
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Facebook users spent about 23 min/day posting, commenting, and replying, while Twitter users spent about 40 min/day on similar activities: tweets, retweets, and replies.
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The number of interactions on Facebook was fewer than on Twitter.
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A majority of the activities for Facebook users was responding to content, while for Twitter users, it was sharing content with others.
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The Twitter users did activities on Twitter several times for all of the time periods, while Facebook users did activities on Facebook several times during the 13:00–18:00 and 18:00–24:00 time periods.
Key Components for SNS Addiction.
Since the Facebook and Twitter platforms and usages are different, we separately analyzed the data from them (including the combination from the questionnaire data) to find the factors associated with SNS addiction.
As for the Facebook variables, the Mann-Whitney U test and logistic regression analysis for both IAT and BFAS indicated that the Facebook variables differing excessive users from normal users. All of the variables that influence BFAS also influenced IAT. As for the Twitter variables, the Mann-Whitney U test and logistic regression analysis indicated that no variables can distinguish between excessive and normal users for IAT. On the contrary, the ratio of usage after the midnight period was the Twitter variable that separated excessive users from normal users for BFAS.
Based on this study’s results, the following are the candidates of the key components for SNS addiction:
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the amount of activity (posting, commenting, and replying) on Facebook
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the ratio of posting videos on Facebook
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the ratio of usage on Facebook in the 18:00–24:00 period
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the ratio of usage on Twitter after the midnight period
Similarly, some candidates in this finding correspond with the results from our previous study [6].
Restriction of SNS APIs.
SNS APIs are insufficient to capture all of the activities in Facebook and Twitter, especially reading and the time of action like/favorite. Graph APIs limit access to Facebook data even if users grant permission for it. The data on a user’s timeline can only be retrieved through APIs. REST APIs limit the number of request operations and response data. As a result, the data obtained by APIs are insufficient to describe all of the aspects of user behavior in Facebook and Twitter and cannot be retrieved over long-time periods. However, using APIs is the most common way to directly retrieve data from SNSs, and users are noticed about data-access.
To improve data analysis, future work will use an alternative method, for example, web log analysis.
6 Conclusion
This research is conducted to assess the symptoms of excessive SNS usage by studying user behavior and emotion in SNSs. We collected data from undergraduate students in Thailand, retrieved them from Facebook and Twitter (including a combination of data from questionnaires), and statistically analyzed them to clarify SNS usage behaviors and factors associated with SNS addiction. This study’s analytical results identified the candidates of key components of SNS addiction.
However, employing SNS APIs is insufficient to capture all Facebook and Twitter activities due to the restriction of APIs. Therefore, further studies will employ web log analysis including the combination with questionnaires and SNS APIs to improve data analysis.
Finally, we will apply our analytic results for detecting the symptoms of excessive SNS usage and use our research’s outcome for developing prevention strategies to increase the awareness of the risks of excessive SNS usage.
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Acknowledgements
We thank Pannee Lumwanwong and Sirirat Weerachatyanukul, the lecture of School of Information Technology and Innovation, Bangkok University, Thailand for IAT-Thai version. We also thank the students of Thai-Nichi Institute of Technology for the participants
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Intapong, P., Charoenpit, S., Achalakul, T., Ohkura, M. (2017). Assessing Symptoms of Excessive SNS Usage Based on User Behavior and Emotion. In: Meiselwitz, G. (eds) Social Computing and Social Media. Human Behavior. SCSM 2017. Lecture Notes in Computer Science(), vol 10282. Springer, Cham. https://doi.org/10.1007/978-3-319-58559-8_7
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