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  • 1 Marist College, USA
  • | 2 Southern Illinois University Carbondale, USA
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Background and aims

Although social networking sites brought giant convenience, many negative effects on users’ psychological well-being need more investigation. This study used a survey to examine Facebook addiction and state anxiety without Facebook. As research shows gender is related to trait anxiety and may interact with trait anxiety to influence state anxiety, we also assess the interaction effect between gender and trait anxiety.

Methods

A total of 526 college students in the US participated in the survey. A systematic sampling method was used and an e-mail invitation with the link of the survey was sent to every third student on the students’ e-mail list. Study measures included demographics, trait anxiety, Facebook intensity, different Facebook activities, Facebook addiction, and state anxiety without Facebook. Hierarchical multiple regression was run to test how trait anxiety, gender, Facebook intensity, and different types of Facebook activities predict Facebook addiction and state anxiety.

Results

Facebook use intensity predicts Facebook addiction (β = 0.573, p < .001) and state anxiety (β = 0.567, p < .001). Facebook use for broadcasting positively predicts Facebook addiction (β = 0.200, p < .01) and state anxiety (β = 0.171, p < .01). Trait anxiety positively predicts Facebook addiction (β = 0.121, p < .05) and state anxiety (β = 0.119, p < .05). Gender interacts with trait anxiety and jointly predicts Facebook addiction (β = 0.201, p < .01).

Discussion and conclusions

Trait anxiety, Facebook intensity, and broadcasting behavior on Facebook positively predict Facebook addiction and state anxiety. Moreover, gender interacts with trait anxiety, so that the gender difference in Facebook addiction is significant only when trait anxiety is low.

Abstract

Background and aims

Although social networking sites brought giant convenience, many negative effects on users’ psychological well-being need more investigation. This study used a survey to examine Facebook addiction and state anxiety without Facebook. As research shows gender is related to trait anxiety and may interact with trait anxiety to influence state anxiety, we also assess the interaction effect between gender and trait anxiety.

Methods

A total of 526 college students in the US participated in the survey. A systematic sampling method was used and an e-mail invitation with the link of the survey was sent to every third student on the students’ e-mail list. Study measures included demographics, trait anxiety, Facebook intensity, different Facebook activities, Facebook addiction, and state anxiety without Facebook. Hierarchical multiple regression was run to test how trait anxiety, gender, Facebook intensity, and different types of Facebook activities predict Facebook addiction and state anxiety.

Results

Facebook use intensity predicts Facebook addiction (β = 0.573, p < .001) and state anxiety (β = 0.567, p < .001). Facebook use for broadcasting positively predicts Facebook addiction (β = 0.200, p < .01) and state anxiety (β = 0.171, p < .01). Trait anxiety positively predicts Facebook addiction (β = 0.121, p < .05) and state anxiety (β = 0.119, p < .05). Gender interacts with trait anxiety and jointly predicts Facebook addiction (β = 0.201, p < .01).

Discussion and conclusions

Trait anxiety, Facebook intensity, and broadcasting behavior on Facebook positively predict Facebook addiction and state anxiety. Moreover, gender interacts with trait anxiety, so that the gender difference in Facebook addiction is significant only when trait anxiety is low.

Introduction

Social networking sites (SNSs) are becoming increasingly influential. Statistics show that 68% of the US adults are Facebook users (Smith & Anderson, 2018) and over 70% of Facebook users log in every day (Chaffey, 2017). Moreover, more than 350 million people around the world are believed to meet the clinical definition of an addiction because of their Facebook habits (Gaille, 2017). Studies revealed that Facebook addiction could activate same brain areas as drugs (Castillo, 2015). Thus, it is essential to understand the underlying mechanism behind Facebook addiction (Andreassen & Pallesen, 2014; Przepiorka & Blachnio, 2016). This study used a survey to examine how gender, trait anxiety, Facebook use intensity, and different types of uses contribute to Facebook addiction and state anxiety without Facebook.

SNS addiction

Addiction refers to “repeated failures to refrain from drug use despite prior resolutions to do so” (West & Brown, 2013, p. 7). Recently, addiction to SNSs attracts scholars’ attention. SNS addiction was defined as being overly concerned about SNSs or driven by a strong motivation to log onto SNSs, so that it impairs normal social activities and/or psychological health and well-being (Andreassen & Pallesen, 2014). Behavioral patterns of obsessively checking SNS have been studied in relation to usage patterns, motivations, and negative consequences (Andreassen et al., 2013; Andreassen, Torsheim, Brunborg, & Pallesen, 2012). These studies maintain that not only the overuse of SNSs may lead to SNS addiction (Andreassen et al., 2013), but also young generation’s addiction to social media may be suffering from intrapsychic conflict.

Regarding the heavy use of SNSs, scholars have expressed their concerns with virtual spaces’ impacts on actual health issues (O’Keeffe & Clarke-Pearson, 2011). Studies show that SNSs are likely to engender negative consequences on mental health, including anxiety, insomnia, and decrease in real-life communities (Kuss & Griffiths, 2011; Pantic, 2014). “Facebook depression” is one of such mental health issues. As a symptom which happens when users spend a substantial and excessive amount of time on Facebook, Facebook depression begins to exhibit classic symptoms of depression (O’Keeffe & Clarke-Pearson, 2011).

Studies demonstrate that SNS addiction needs to be scrutinized with different approaches (Kuss, Griffiths, Karila, & Billieux, 2014). Research shows that determinants of Facebook addiction include gender (Andreassen et al., 2013; Turel, He, Xue, Xiao, & Bechara, 2014), time spent on the Internet (Hormes, Kearns, & Timko, 2014; Marino, Gini, Vieno, & Spada, 2018a, 2018b), motivations such as relationship maintenance, entertainment, and companionship (Ryan, Chester, Reece, & Xenos, 2014), and personalities such as neuroticism and consciousness (Andreassen et al., 2012). However, Marino et al.’s (2018a, 2018b) meta-analysis on Facebook addiction suggested that future research should examine frequency of different Facebook activities and other personality factors.

Anxiety

Anxiety was defined as a negative emotion characterized by an unpleasant state of inner turmoil, often accompanied by nervous behavior (Seligman, Walker, & Rosenhan, 2001; Spielberger, 1972). In contrast with fear, which is induced by real or immediate threat, anxiety is an unpleasant feeling or expectation of future threat (American Psychiatric Association, 2013). Anxiety may be induced when there is a lack of enough information to make appropriate judgments or decisions about the changing environment (Xie & Newhagen, 2014).

Spielberger (1972) distinguished trait anxiety from state anxiety. Trait anxiety is a personality that runs in one’s genes and has been considered as a permanent psychological characteristic (Spielberger, 1966). It refers to the stable tendency to attend to and experience negative emotions including worries and fear across various situations (Gidron, 2013). However, state anxiety means how anxious people feel at a moment (Lazarus, 1991). Psychological studies demonstrated that trait anxiety is a strong indicator of state anxiety (Li & Lopez, 2005). Xie and Newhagen’s (2014) research showed that subjects’ trait anxiety predicts state anxiety induced by mobile phone.

  • H1: People with higher level of trait anxiety will report higher level of state anxiety without Facebook.

As an aversive response to stress, anxiety has been linked to addiction. Scholars found that people with higher level of trait anxiety are more likely to develop addictions to substances (Litt, Cooney, & Morse, 2000). Kushner, Krueger, Frye, and Peterson’s (2008) quasi-experiment revealed the co-occurrence between anxiety disorders and substance use disorders. Lai, Cleary, Sitharthan, and Hunt (2015) revealed strong correlations between anxiety and alcohol and drug use. Mehroof and Griffiths’ (2010) study showed that trait anxiety has a positive relationship with online gaming addiction. In the realm of SNS, research has revealed the association between problematic Facebook use and signs of psychological distress including anxiety and depression (Koc & Gulyagci, 2013; Marino et al., 2018b; Prizant-Passal, Shechner, & Aderka, 2016). The reason is people with high psychological vulnerability are more likely to rely on SNS to fulfill their social needs or to regulate their mood online (Hong, Huang, Lin, & Chiu, 2014; Satici, Saricali, Statici, & Capan, 2014). However, most of the research focuses on social anxiety (Prizant-Passal et al., 2016; Statici et al., 2014), not trait anxiety.

  • H2: People with higher level of trait anxiety will report higher level of Facebook addiction.

It is also possible that people who are more addicted to Facebook will experience higher level of state anxiety without Facebook due to dependence on the technologies. Park and Choi (2015) found that compared with people in the low addiction or normal group, those with higher smartphone addiction reported higher level of state anxiety when their phone was taken away. Hawi and Samaha’s (2017) survey revealed that people with smartphone addiction demonstrated greater odds of having high state anxiety compared to those who were not addicted.

  • H3: People with higher level of Facebook addiction will report higher level of state anxiety without Facebook.

Gender and SNS addiction

Gender has been found to be related to addiction to new technologies (Ikeda & Nakamura, 2014). However, there has been no consensus regarding the relationship between gender and SNS addiction. While some research show a positive correlation between male users and addiction to games on social media (Smahel, Blinka, & Ledabyl, 2008), scholars also found that females present more addictive symptoms in Facebook use (Marino et al., 2018a; Thompson & Lougheed, 2012; Turel et al., 2014). Other scholars, nonetheless, found such gender difference in Facebook addiction small or even null (Beyens, Frison, & Eggermont, 2016; Koc & Gulyagci, 2013). Marino et al.’s (2018a) recent meta-analysis of 56 studies revealed a small, higher problematic Facebook use in female. Given such inconsistent findings, scholars suggest that further research is needed to examine gender differences in SNS addiction (Griffiths, 2010).

  • H4: Female will demonstrate higher level of (a) Facebook addiction and (b) state anxiety.

Moreover, Koc and Gulyaci (2013) suggested that the inconsistent findings about gender differences in SNS addiction warrant further research to examine if gender interacts with other variables in affecting SNS addiction. In fact, research has revealed the interaction effect between gender and personalities on SNS use. For instance, Simoncic, Kuhlman, Vargas, Houchins, and Lopez-Duran’s (2014) study detected a three-way interaction effect between gender, neuroticism, and Facebook activity on depressive symptoms. Barker (2009) also found the interaction between gender and self-esteem predicts different SNS activities.

Research shows that females are more likely to develop anxiety disorders than males in both adolescents (Lewinsohn, Gotlib, Lewinsohn, Seeley, & Allen, 1998) and adults (Donner & Lowry, 2013) due to genetic factors, physiological reactivity, hormonal influences, and evolution influences (McLean & Anderson, 2009). Hishinuma, Miyamoto, Nishimura, and Nahulu (2000) found that female had higher level of trait anxiety than male. Moreover, gender interacted with trait anxiety in predicting state anxiety. Given the connection between gender and trait anxiety, we have the following question:

  • RQ1: Will gender interact with trait anxiety to predict Facebook addiction and state anxiety without Facebook?

Social media use and Facebook addiction

Research has identified the positive correlation between frequent Facebook use and Facebook addiction (Karaiskos, Tzavellas, Balta, & Paparrigopoulos, 2010). Thompson and Lougheed (2012) found people who spend more minutes on Facebook are more inclined to feel addicted to the site. Hong et al.’s (2014) study revealed that the amount of time spent on Facebook positively predicts Facebook addictive tendency. Using objective measures, Marino, Finos, Vieno, Lenzi, and Spada (2017) found Facebook addiction is related to frequency of Facebook activities.

  • H5: Facebook use intensity will be positively related to (a) Facebook addiction and (b) state anxiety.

Different types of Facebook activities

The uses and gratifications theory (Elihu, Blumler, & Gurevitch, 1974) suggests that people may use media platforms in different ways for different motivations. As SNSs provide multiple functions, scholars also start to explore the different psychological and behavioral aspects of different types of SNS activities (Marino et al., 2018b). Burke, Kraut, and Marlow (2011) distinguished three types of activities on SNSs. The first one is directed communication with individual friends, including personal, one-on-one exchanges such as sending messages, posting comments on friends’ walls or photos, tagging, “liking,” and synchronous chat. The second type of SNS activities is passive consumption of social news, which happens when one reads others’ updates. The third type of SNS activity is broadcasting, including status updating, sharing pictures or videos, and writing notes. Broadcasting usually is not targeted at a particular person. Instead, it happens when one writes or posts on SNS for general SNS users to read (Burke & Kraut, 2016).

Scholars have suggested that beyond the frequency and time spent on Facebook, analysis of the relationship between different types of Facebook activities and Facebook addiction is needed (Marino et al., 2018a; Ryan et al., 2014; Ryan, Reece, Chester, & Xenos, 2016). Ryan et al. (2014) found the use of Facebook for social interaction, passing time, entertainment, companionship, and escape may be related to the development of Facebook addiction. Dhaha (2013) found that social interaction and entertainment are two strongest predictors of Facebook addiction. However, neither of these studies touches on the specific activities on SNSs. Griffiths (2010) found that playing games on SNS is positively linked to SNS addiction. Marino et al. (2017) found problematic Facebook users and non-problematic users differ in several objective behaviors, including the number of events attended, the number of friend requests sent, status update, “like,” and shared posts. Ryan et al. (2016) distinguished low engagement users from high engagement users in browsing, social activities, and gaming. As SNS is growing fast and more and more features are available for the users, it is essential to understand how different activities on SNS are related to SNS addiction.

  • RQ2: How will different types of SNS activities predict Facebook addiction and state anxiety without Facebook?

Methods

Participants

A survey with 526 students enrolled in a large Midwestern university in the US was conducted in fall 2015. The college students were chosen for several reasons. Statistics show that people between 18 and 29 years old are the largest group among social media users (Greenwood, Perrin, & Duggan, 2016). Moreover, university students are more vulnerable to Internet addiction because of their flexible schedule and academic pressure (Kandell, 1998). Research also documented college students’ addiction to social media (McCrary, 2016).

To ensure the representativeness of the sample, the complete e-mail list of all enrolled students was obtained from the university under the university’s Freedom of Information Act Policy. Using a systematic sampling method, an e-mail invitation with the link of the survey was sent to 6,000 students, accounting for one third of the student population. To increase the response rate, two reminder e-mails were sent to the respondents 1 and 2 weeks after the initial invitation. Finally, 526 students completed the survey. The average age of the sample was 24.21 (SD = 5.92). Females accounted for 59% of the sample and 41% were male. The majority of the sample was White (68.4%), followed by Asian (12.7%), Hispanic (7.0%), and Black and African Americans (6.3%).

Measures

Facebook intensity

It was measured using Facebook Intensity Scale (Ellison, Steinfield, & Lampe, 2007), which includes six closed-ended questions such as “Facebook is part of my everyday activity” on a 5-point Likert scale [ranging from 1 (strongly disagree) to 5 (strongly agree)]. There are two more open-ended questions measuring number of Facebook friends and time spent on Facebook. Answers to these questions were evenly divided into five categories. All scores were standardized, and a Facebook use intensity score was created by calculating the mean of the eight items (M = 2.85, SD = 1.04, Cronbach’s α = .83).

Facebook addiction

It was measured using Bergen Facebook Addiction Scale (BFAS; Andreassen et al., 2012; Brailovskaia & Margraf, 2017). The BFAS has six items such as “You feel an urge to use Facebook more and more” measured on a 5-point Likert scale [ranging from 1 (very rarely) to 5 (very often)]. Answers were averaged (M = 3.15, SD = 0.71, Cronbach’s α = .87).

Trait anxiety

It was measured using State-Trait Anxiety Inventory (STAI; Spielberger, 2010) – Trait Anxiety, ranging from 1 (not at all) to 4 (very much). A new index, trait anxiety, was formed by averaging answers to these 20 questions (M = 2.59, SD = 0.62, Cronbach’s α = .90).

State anxiety

To reduce the respondents’ fatigue, we used the six-item Short-Form State Anxiety Scale (Marteau & Bekker, 1992) instead of the original 20-item STAI-State Anxiety (Spielberger, 2010). The Short-Form State Anxiety Scale has been demonstrated to be a highly reliable and valid instrument (Tluczek, Henriques, & Brown, 2009). The subjects were asked: “If you have lost access to your Facebook account for 48 hr, how would you feel right now, at this moment?” Question items include: “I feel tense now,” etc., measured on a 4-point Likert scale [ranging from 1 (not at all) to 4 (very much)]. A new index, state anxiety without Facebook, was formed by averaging the item scores (M = 2.01, SD = 0.73, Cronbach’s α = .83).

Different types of Facebook activities were adopted from previous studies (Burke et al., 2011; Xie & Kang, 2015; Zhong, 2013), asking the respondents how frequently they use SNS to share pictures, update status, etc., using a 5-point Likert scale [ranging from 1 (not at all) to 5 (very frequently)].

Principal component analysis (PCA) with varimax rotation was performed and generated two factors [PCA is a dimension-reduction technique. We used PCA in this study to condense the information in different types of Facebook activities into a smaller set of new composite variables that represent most of the information in the original set of variables (Dunteman, 1989]. The goal is to reduce the dimensionality of different types of Facebook activities to facilitate data interpretation). Following Burke et al. (2011) and Xie and Kang (2015), the first type of SNS activities was named directed communication, including sending private messages to friends, sending messages to groups, leaving comments on friends’ wall or pictures, and liking friends’ posts or pictures (Eigenvalue = 3.172, Cronbach’s α = .89). The second type of SNS activities was named broadcasting, including status update, sharing pictures or videos, and writing notes (Eigenvalue = 1.953, Cronbach’s α = .91; Table 1).

Table 1.

Factor analysis of different types of Facebook use

VariablesFactor loadingsa
Directed communicationBroadcasting
Sending private messages to friends0.848
Commenting on friends’ wall, photo, or video0.844
Liking friends’ post or pictures0.815
Sending group instant messages0.810
Updating status0.814
Sharing pictures0.800
Sharing videos0.751
Writing notes0.689
Percent variance explained44.53427.555
Eigenvalue3.1721.953
Cronbach’s α.89.91

Note. aQuartimax rotation was employed for the final solution.

Demographics including gender, age, house income, and education were measured.

Procedure

An online survey was created on the university server. The link of the survey was sent to the participants’ e-mail. The participants completed the survey at their convenience.

Statistical analysis

To test the hypotheses and answer the research questions, two hierarchical linear regression analyses were performed by using IBM SPSS 25.0 (IMB SPSS Inc., Chicago IL, USA). Parameter estimation was performed through ordinary least squares. Facebook addiction and state anxiety without Facebook were entered as the dependent variables, respectively. For both models, demographics were entered in the first block, followed by trait anxiety in the second block. Facebook use intensity was entered in the third block and different types of Facebook use in the fourth block. The interaction term between gender and trait anxiety was entered as the last step. To avoid multicollinearity, trait anxiety was centered by subtracting the mean value from the original value. The interaction term between trait anxiety and gender was calculated by multiplying the centered value of trait anxiety with gender.

Ethics

The participants were provided written informed consent after clicking on the survey link. They had to indicate whether they were willing to participate in the survey and they were above 18 years old to proceed. This study was approved by the institutional review board of Southern Illinois University Carbondale.

Results

Table 2 shows that trait anxiety was a positive predictor for both Facebook addiction (β = 0.121, p < .05, 95% CI [0.015, 0.037]) and state anxiety without Facebook (β = 0.119, p < .05, 95% CI [0.002, 0.035]), supporting H1 and H2. Trait anxiety explained 3.2% of the variances in Facebook addiction (ΔR2 = .032, p < .05) and 1.3% of the variances in state anxiety (ΔR2 = .013, p < .05). People with higher level of Facebook addiction reported higher level of state anxiety without Facebook (β = 0.291, p < .001, 95% CI [0.202, 0.338]), supporting H3.

Table 2.

Hierarchical regression predicting Facebook addiction and state anxiety without Facebook

Facebook addictionState anxiety without Facebook
βtβt
Block 1: Demographics
Male−0.0851.513−0.051−0.891
Age0.0200.3140.0150.237
Education0.1021.5400.0130.190
Household income−0.045−0.7910.0210.362
White−0.083−1.612−0.092−1.569
ΔR2.025.011
Block 2: Personality
Trait anxiety0.1211.926*0.1191.808*
ΔR2.032*.013*
Block 3: Facebook use
Facebook use intensity0.57312.258***0.56711.869***
ΔR2.318***.312***
Block 4: Different types of Facebook activities
Broadcasting0.2003.013**0.1712.493**
Directed communication0.0370.696−0.091−1.665
ΔR2.035**.015*
Block 5: Facebook addiction
Facebook addiction0.2915.226***
ΔR2.047***
Block 5: Gender × Trait
Gender × Trait anxiety0.2043.161**−0.036−0.954
ΔR2.032**.003
Total ΔR2.442.401

Note. β: standardized regression coefficients.

*p < .05. **p < .01. ***p < .001.

Gender did not predict either Facebook addiction or state anxiety, and both H4a and H4b were rejected. However, as shown in Figure 1, the interaction between gender and trait anxiety was a significant predictor of Facebook addiction (β = 0.204, p < .01, 95% CI [0.101, 0.203]). Post-hoc analysis shows that among people with lower level of trait anxiety, females had higher level of Facebook addiction (M = 2.53, SD = 0.73) than males (M = 2.18, SD = 0.68), t(282) = 2.65, p < .01. Such gender difference did not exist among those with higher level of trait anxiety.

Figure 1.
Figure 1.

Predicting Facebook addiction by gender and trait anxiety

Citation: Journal of Behavioral Addictions J Behav Addict 8, 1; 10.1556/2006.8.2019.09

The more intensively people used Facebook, the more addicted they were to it (β = 0.573, p < .001, 95% CI [0.201, 0.357]) and the more anxious they felt when separated from Facebook (β = 0.567, p < .001, 95% CI [0.325, 0.548]), supporting both H5a and H5b. Facebook use intensity was the strongest predictor and explained 31.8% of the variances in Facebook addiction (ΔR2 = .318, p < .001) and 31.2% of the variances in state anxiety without Facebook (ΔR2 = .312, p < .001).

Regarding RQ2, hierarchical regression analysis showed that SNS use for broadcasting such as status updating, sharing photos, or sharing videos increased both Facebook addiction (β = 0.200, p < .01, 95% CI [0.010, 0.066]) and Facebook state anxiety (β = 0.171, p < .01, 95% CI [0.012, 0.093]).

Discussion and Conclusions

Using a survey with 526 college students, this study tried to assess Facebook addiction and state anxiety without Facebook and examine the relationship between Facebook use, personalities, and Facebook addiction and anxiety. Although previous studies suggest that certain personality traits may be important in the acquisition and development of Facebook addiction, most of those studies focused on the Big-Five personalities (Hong et al., 2014; Marino et al., 2018a; Ong et al., 2011; Ross et al., 2009; Tang, Chen, Yang, Chung, & Lee, 2016). Marino et al. (2018a) indicated that research efforts should be devoted to other types of personalities. One novel finding in this study is that we differentiated trait anxiety from state anxiety and identified trait anxiety as a strong personality factor for Facebook addiction and state anxiety without Facebook, echoing previous psychological research (Mehroof & Griffiths, 2010). It may be because people with higher level of trait anxiety tend to use Facebook to escape from the stress in real life and are more likely to be addicted to this virtual world (Hong et al., 2014; Satici et al., 2014). Such results also show that people with higher level of trait anxiety are more likely to feel anxious when they are separated from SNSs. Given the lack of the attention to trait anxiety in addiction research, we suggest that trait anxiety deserves more attention in technology addiction research.

Another novel finding from this study is that we found trait anxiety interacts with gender in predicting Facebook addiction. When trait anxiety is low, women are more addicted to Facebook than men, consistent with the findings from previous research (Andreassen et al., 2013; Thompson & Lougheed, 2012). However, when trait anxiety is high, such gender difference does not exist. This suggests that men are more resistant to Facebook addiction when their trait anxiety is low. For men with high trait anxiety, such resistance disappears. Marino et al. (2018b) suggested that future research should examine individual and contextual factors that may serve as a moderator. These findings suggest that the relationship between gender and Facebook addiction is complicated and trait anxiety can play a significant moderating role and thus deserves more examination.

Consistent with previous research using both self-report (Hong et al., 2014; Thompson & Lougheed, 2012) and objective measures (Marino et al., 2018a, 2018b), the results in this study showed that those who use Facebook more intensively are more addicted to Facebook and report higher level of state anxiety without Facebook. In fact, previous studies argued that the frequency of use is part of the problematic Facebook use (Hormes et al., 2014; Marino et al., 2018a, 2018b). It is worth noting that this study adopted the Facebook use intensity scale, which not only measures Facebook use frequency, but also focuses more on the psychological and emotional attachment to Facebook (Ellison et al., 2007). This result suggests that those who spend more time on Facebook and psychologically rely on Facebook should be more aware of the potential mental health problem.

We categorized different types of SNS activities and examined their relationship with Facebook addiction. Consistent with Marino et al.’s (2017) findings, we found that updating status and sharing photos and videos strongly predicts Facebook addiction, and this relationship remains robust even after controlling for Facebook use intensity. However, in contrast with Marino et al.’s (2017) findings, wall activities such as “liking” or commenting are not related to Facebook addiction. This discrepancy may lay in the fact that Facebook activities were measured using self-report in this study, while Marino et al. (2017) used objective measures by downloading Facebook users’ data, which are more objective and reliable. These findings reflect that different types of activities on SNSs may be associated with different psychological aspects of SNSs (Burke et al., 2011; Ryan et al., 2014, 2016).

However, this study has several limitations. First, we used single center data resources, who were university students, implying that the results cannot be generalized to other demographic groups. Second, the study had medium sample size (N = 526), which may influence the external validity of the study. However, meta-analysis on Facebook addiction showed only 15 out of the 52 published studies used a sample larger than 500 people (Marino et al., 2018b), suggesting most of the research on Facebook addiction still use a relatively small sample size. Third, the cross-sectional design precludes the ability to make conclusions of causality (Pantic, 2014). Thus, it is unclear whether intensive use of Facebook increases Facebook addiction or if Facebook addiction increases time spent on SNSs. Marino et al. (2018a, 2018b) found that this domain of research is still dominated by cross-sectional studies. Future research may adopt longitudinal or experimental design to test causalities. Fourth, as data from this study depended on subjects’ self-report, the validity of the measurements about mental health variables may be influenced by the social desirability problem (Fisher, 1993).

In conclusion, this study explored the predictors of Facebook addiction and state anxiety, including trait anxiety, gender, Facebook intensity, and different types of Facebook activities. Our findings have several implications. First, we identified trait anxiety not only predicts Facebook addiction and state anxiety, but also moderates the relationship between gender and Facebook addiction. Facebook addiction, in turn, is positively related to state anxiety without Facebook. This may suggest that addiction to the virtual world such as Facebook may affect people’s real life (O’Keeffe & Clarke-Pearson, 2011) and psychological well-being, especially among those with highly anxious personality. From a clinical perspective, this would imply that psychiatrists and counseling psychologists should pay more attention to psychologically vulnerable groups, especially those with high trait anxiety, as they are at special risks of Facebook addiction and state anxiety. Second, although overall Facebook use may lead to Facebook addiction, people need to limit their time in certain types of behavior such as status updating and sharing user-generated content because such activities may increase their addictive symptoms and state anxiety when separated from Facebook. For the younger generation, parents and educators need to monitor not only the total time that they spend online, but also the specific online activities. Third, future studies may adopt physiological measurements to evaluate Facebook addiction and anxiety levels, or data mining techniques to obtain objective user behavior data on Facebook.

Authors’ contribution

Dr. WX was involved in literature review, questionnaire development, data collection, statistical analysis, results, and discussions. Dr. KK was involved in literature review, questionnaire development, and discussions.

Conflict of interest

There is no conflict of interest between the authors.

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  • Donner, N. C., & Lowry, C. A. (2013). Sex differences in anxiety and emotional behavior. Pflügers Archiv – European Journal of Physiology, 465(5), 601626. doi:10.1007/s00424-013-1271-7

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  • Chaffey, D. (2017). Global social media research summary 2017. Retrieved April 27, 2017, from https://www.smartinsights.com/social-media-marketing/social-media-strategy/new-global-social-media-research/

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    • Export Citation
  • Dhaha, I. (2013). Predictors of Facebook addiction among youth: A structural equation modeling. Journal of Social Sciences, 2(4), 186195.

    • Search Google Scholar
    • Export Citation
  • Donner, N. C., & Lowry, C. A. (2013). Sex differences in anxiety and emotional behavior. Pflügers Archiv – European Journal of Physiology, 465(5), 601626. doi:10.1007/s00424-013-1271-7

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    • Export Citation
  • Dunteman, G. H. (1989). Principal components analysis. London, UK: Sage.

  • Elihu, K., Blumler, J., & Gurevitch, M. (1974). Utilization of mass communication by the individual. In J. Blumler & E. Katz (Eds.), The uses of mass communications: Current perspectives on gratifications research (pp. 1931). Beverly Hills, CA: Sage.

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    • Export Citation
  • Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook “friends”: Social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication, 12(4), 11431168. doi:10.1111/j.1083-6101.2007.00367.x

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    • Search Google Scholar
    • Export Citation
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    • Export Citation
  • Gaille, B. (2017). 43 remarkable Facebook addiction statistics. Retrieved May 29, 2017, from https://brandongaille.com/42-remarkable-facebook-addiction-statistics/

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    • Export Citation
  • Griffiths, M. D. (2010). Gaming in social networking sites: A growing concern? World Online Gambling Law Report, 9(5), 1213.

  • Hawi, N. S., & Samaha, M. (2017). Relationships among smartphone addiction, anxiety, and family relations. Behaviour & Information Technology, 36(10), 10461052. doi:10.1080/0144929X.2017.1336254

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hishinuma, E. S., Miyamoto, R. H., Nishimura, S. T., & Nahulu, L. B. (2000). Differences in State-Trait Anxiety Inventory scores for ethnically diverse adolescents in Hawaii. Cultural Diversity and Ethnic Minority Psychology, 6(1), 7383. doi:10.1037/1099-9809.6.1.73

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hormes, J. M., Kearns, B., & Timko, C. A. (2014). Craving Facebook? Behavioral addiction to online social networking and its association with emotion regulation deficits. Addiction, 109(12), 20792088. doi:10.1111/add.12713

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hong, F.-Y., Huang, D.-H., Lin, H.-Y., & Chiu, S.-L. (2014). Analysis of the psychological traits, Facebook usage, and Facebook addiction model of Taiwanese university students. Telematics and Informatics, 31(4), 597606. doi:10.1016/j.tele.2014.01.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ikeda, K., & Nakamura, K. (2014). Association between mobile phone use and depressed mood in Japanese adolescents: A cross-sectional study. Environmental Health and Preventive Medicine, 19(3), 187193. doi:10.1007/s12199-013-0373-3

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kandell, J. (1998). Internet addiction on campus: The vulnerability of college students. CyberPsychology & Behavior, 1(1), 1117. doi:10.1089/cpb.1998.1.11

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Karaiskos, D., Tzavellas, E., Balta, G., & Paparrigopoulos, T. (2010). P02-232 – Social network addiction: A new clinical disorder? European Psychiatry, 25, 855. doi:10.1016/S0924-9338(10)70846-4

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koc, M., & Gulyagci, S. (2013). Facebook addiction among Turkish college students: The role of psychological health, demographic, and usage characteristics. Cyberpsychology, Behavior, and Social Networking, 16(4), 279284. doi:10.1089/cyber.2012.0249

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kushner, M. G., Krueger, R., Frye, B., & Peterson, J. (2008). Epidemiological perspectives on co-occurring anxiety disorder and substance use disorder. In S. Stewart & P. Conrod (Eds.), Anxiety and substance use disorders (pp. 317). Boston, MA: Springer.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuss, D. J., & Griffiths, M. D. (2011). Online social networking and addiction – A review of the psychological literature. International Journal of Environmental Research and Public Health, 8(9), 35283552. doi:10.3390/ijerph8093528

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuss, D. J., Griffiths, M. D., Karila, L., & Billieux, J. (2014). Internet addiction: A systematic review of epidemiological research for the last decade. Current Pharmaceutical Design, 20(25), 40264052. doi:10.2174/13816128113199990617

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lai, H., Cleary, M., Sitharthan, T., & Hunt, G. E. (2015). Prevalence of comorbid substance use, anxiety and mood disorders in epidemiological surveys, 1990–2014: A systematic review and meta-analysis. Drug and Alcohol Dependence, 154, 113. doi:10.1016/j.drugalcdep.2015.05.031

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lazarus, R. S. (1991). Emotion and adaptation. London, UK: Oxford University Press.

  • Lewinsohn, P. M., Gotlib, I. H., Lewinsohn, M., Seeley, J. R., & Allen, N. B. (1998). Gender differences in anxiety disorders and anxiety symptoms in adolescents. Journal of Abnormal Psychology, 107(1), 109117. doi:10.1037/0021-843X.107.1.109

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, H., & Lopez, V. (2005).Do trait anxiety and age predict state anxiety of school-age children? Journal of Clinical Nursing, 14(9), 10831089 . doi:10.1111/j.1365-2702.2005.01223.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Litt, M. D., Cooney, N. L., & Morse, P. (2000). Reactivity to alcohol-related stimuli in the laboratory and in the field: Predictors of craving in treated alcoholics. Addiction, 95(6), 889900. doi:10.1046/j.1360-0443.2000.9568896.x

    • Crossref
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The author instruction is available in PDF.
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Dr. Zsolt Demetrovics
Institute of Psychology, ELTE Eötvös Loránd University
Address: Izabella u. 46. H-1064 Budapest, Hungary
Phone: +36-1-461-2681
E-mail: jba@ppk.elte.hu

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2020  
Total Cites 4024
WoS
Journal
Impact Factor
6,756
Rank by Psychiatry (SSCI) 12/143 (Q1)
Impact Factor Psychiatry 19/156 (Q1)
Impact Factor 6,052
without
Journal Self Cites
5 Year 8,735
Impact Factor
Journal  1,48
Citation Indicator  
Rank by Journal  Psychiatry 24/250 (Q1)
Citation Indicator   
Citable 86
Items
Total 74
Articles
Total 12
Reviews
Scimago 47
H-index
Scimago 2,265
Journal Rank
Scimago Clinical Psychology Q1
Quartile Score Psychiatry and Mental Health Q1
  Medicine (miscellaneous) Q1
Scopus 3593/367=9,8
Scite Score  
Scopus Clinical Psychology 7/283 (Q1)
Scite Score Rank Psychiatry and Mental Health 22/502 (Q1)
Scopus 2,026
SNIP  
Days from  38
sumbission  
to 1st decision  
Days from  37
acceptance  
to publication  
Acceptance 31%
Rate  

2019  
Total Cites
WoS
2 184
Impact Factor 5,143
Impact Factor
without
Journal Self Cites
4,346
5 Year
Impact Factor
5,758
Immediacy
Index
0,587
Citable
Items
75
Total
Articles
67
Total
Reviews
8
Cited
Half-Life
3,3
Citing
Half-Life
6,8
Eigenfactor
Score
0,00597
Article Influence
Score
1,447
% Articles
in
Citable Items
89,33
Normalized
Eigenfactor
0,7294
Average
IF
Percentile
87,923
Scimago
H-index
37
Scimago
Journal Rank
1,767
Scopus
Scite Score
2540/376=6,8
Scopus
Scite Score Rank
Cllinical Psychology 16/275 (Q1)
Medicine (miscellenous) 31/219 (Q1)
Psychiatry and Mental Health 47/506 (Q1)
Scopus
SNIP
1,441
Acceptance
Rate
32%

 

Journal of Behavioral Addictions
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 850 EUR/article
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription Information Gold Open Access
Purchase per Title  

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
Publication
Programme
2021 Volume 10
Volumes
per Year
1
Issues
per Year
4
Founder Eötvös Loránd Tudományegyetem
Founder's
Address
H-1053 Budapest, Hungary Egyetem tér 1-3.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-5871 (Print)
ISSN 2063-5303 (Online)

Senior editors

Editor(s)-in-Chief: Zsolt DEMETROVICS

Assistant Editor(s): Csilla ÁGOSTON

Associate Editors

  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • Daniel KING (Flinders University, Australia)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • H. N. Alexander LOGEMANN (ELTE Eötvös Loránd University, Hungary)
  • Anikó MARÁZ (Humboldt University of Berlin, Germany)
  • Astrid MÜLLER (Hannover Medical School, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)
  • Attila SZABÓ (ELTE Eötvös Loránd University, Hungary)
  • Róbert URBÁN (ELTE Eötvös Loránd University, Hungary)
  • Aviv M. WEINSTEIN (Ariel University, Israel)

Editorial Board

  • Max W. ABBOTT (Auckland University of Technology, New Zealand)
  • Elias N. ABOUJAOUDE (Stanford University School of Medicine, USA)
  • Hojjat ADELI (Ohio State University, USA)
  • Alex BALDACCHINO (University of Dundee, United Kingdom)
  • Alex BLASZCZYNSKI (University of Sidney, Australia)
  • Kenneth BLUM (University of Florida, USA)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Beáta BÖTHE (University of Montreal, Canada)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Sam-Wook CHOI (Eulji University, Republic of Korea)
  • Damiaan DENYS (University of Amsterdam, The Netherlands)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Heather HAUSENBLAS (Jacksonville University, USA)
  • Tobias HAYER (University of Bremen, Germany)
  • Susumu HIGUCHI (National Hospital Organization Kurihama Medical and Addiction Center, Japan)
  • David HODGINS (University of Calgary, Canada)
  • Eric HOLLANDER (Albert Einstein College of Medicine, USA)
  • Jaeseung JEONG (Korea Advanced Institute of Science and Technology, Republic of Korea)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Emmanuel KUNTSCHE (La Trobe University, Australia)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Michel LEJOXEUX (Paris University, France)
  • Anikó MARÁZ (Eötvös Loránd University, Hungary)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Frederick GERARD MOELLER (University of Texas, USA)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Nancy PETRY (University of Connecticut, USA)
  • Bettina PIKÓ (University of Szeged, Hungary)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Rory C. REID (University of California Los Angeles, USA)
  • Marcantanio M. SPADA (London South Bank University, United Kingdom)
  • Daniel SPRITZER (Study Group on Technological Addictions, Brazil)
  • Dan J. STEIN (University of Cape Town, South Africa)
  • Sherry H. STEWART (Dalhousie University, Canada)
  • Attila SZABÓ (Eötvös Loránd University, Hungary)
  • Ferenc TÚRY (Semmelweis University, Hungary)
  • Alfred UHL (Austrian Federal Health Institute, Austria)
  • Johan VANDERLINDEN (University Psychiatric Center K.U.Leuven, Belgium)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Kimberly YOUNG (Center for Internet Addiction, USA)

 

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