Conceptualising social media addiction: a longitudinal network analysis of social media addiction symptoms and their relationships with psychological distress in a community sample of adults

Abstract

Background: Problematic social media use has been identified as negatively impacting psychological and everyday functioning and has been identified as a possible behavioural addiction (social media addiction; SMA). Whether SMA can be classified as a distinct behavioural addiction has been debated within the literature, with some regarding SMA as a premature pathologisation of ordinary social media use behaviour and suggesting there is little evidence for its use as a category of clinical concern. This study aimed to understand the relationship between proposed symptoms of SMA and psychological distress and examine these over time in a longitudinal network analysis, in order better understand whether SMA warrants classification as a unique pathology unique from general distress.

Method: N = 462 adults (M_age = 30.8, SD_age = 9.23, 69.3% males, 29% females, 1.9% other sex or gender) completed measures of social media addiction (Bergen Social Media Addiction Scale), and psychological distress (DASS-21) at two time points, twelve months apart. Data were analysed using network analysis (NA) to explore SMA symptoms and psychological distress. Specifically, NA allows to assess the 'influence' and pathways of influence of each symptom in the network both cross-sectionally at each time point, as well as over time.

Results: SMA symptoms were found to be stable cross-sectionally over time, and were associated with, yet distinct, from, depression, anxiety and stress. The most central symptoms within the network were tolerance and mood-modification in terms of expected influence and closeness respectively. Depression symptoms appeared to have less of a formative effect on SMA symptoms than anxiety and stress.

Conclusions: Our findings support the conceptualisation of SMA as a distinct construct occurring based on an underpinning network cluster of behaviours and a distinct association between SMA symptoms and distress. Further replications of these findings, however, are needed to strengthen the evidence for SMA as a unique behavioural addiction.

Keywords: Longitudinal network analysis; Psychological distress; Social media addiction.

Fig. 1

Network of the BSMAS symptoms and DASS subscales at time point 1

Fig. 2

Network of the BSMAS symptoms and DASS subscales at time point 2

Fig. 3

Expected Influence across all nodes at time point 1

Fig. 4

Centrality difference tests of Expected Influence at time point 1

Fig. 5

Closeness and betweenness across all nodes at time point 1

Fig. 6

Centrality difference tests of betweenness at time point 1

Fig. 7

Centrality difference tests of closeness at time point 1

Fig. 8

Edges’ difference tests at time point 1

Fig. 9

Bridge Expected Influence Centrality at time point 1

Fig. 10

Bridge Closeness Centrality at time point 1

Fig. 11

Expected Influence across all nodes at time point 2

Fig. 12

Centrality difference tests of Expected Influence at time point 2

Fig. 13

Betweenness across all nodes at time point 2

Fig. 14

Closeness across all nodes at time point 2

Fig. 15

Centrality difference tests of betweenness at time point 2

Fig. 16

Centrality difference tests of closeness at time point 2

Fig. 17

Edges’ difference tests at time point 2

Fig. 18

Bridge Expected Influence Centrality at time point 2

Fig. 19

Bridge Closeness Centrality at time point 2

Fig. 20

Bridge Betweenness Centrality at time point 2