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. 2022 Feb 25:15:100417.
doi: 10.1016/j.abrep.2022.100417. eCollection 2022 Jun.

Predicting comments on Facebook photos: Who posts might matter more than what type of photo is posted

Claudia Marino  1   2 , Ciro Lista  3 , Dario Solari  4 , Marcantonio M Spada  2 , Alessio Vieno  1 , Livio Finos  1
Affiliations

Predicting comments on Facebook photos: Who posts might matter more than what type of photo is posted

Claudia Marino et al. Addict Behav Rep. .

Abstract

The number of likes and comments received to social media posts and images are influential for users' self-presentation and problematic Facebook use. The aim of this study was to highlight the most relevant factors predicting the popularity (i.e., the probability to receive at a least a comment) of Facebook photos based on: (i) Facebook user-related features; (ii) Facebook photo-related features; and (iii) and psychological variables. A mixed approach was used, including objective data extracted from Facebook (regarding users' characteristics and photo features) as well as answers to a questionnaire. Participants were 227 Facebook users (M = 25.01(1.05) years). They were asked to answer a questionnaire and provide a copy of their Facebook profile data. A total of 180,547 photos receiving a total of 122,689 comments were extracted. Results showed that user-related features (Facebook network and activities) were the most relevant in predicting image popularity accurately. It seems that who posts a Facebook photo matters more than the type of photo posted and the psychological profile of the user. Results are discussed within a psychological perspective. Future research should look at the sentiment (positive vs. negative) of the comments received by different types of photos. This is the first study exploring what makes a Facebook photo popular using objective data rather than self-reported frequency of Facebook activity only. Results might advance current methods and knowledge about potential problematic behaviors on social media.

Keywords: Comment; Facebook; Machine learning; Objective data; Photo.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Top 10 feature importance for the User + Psy model.
Fig. 2
Fig. 2
Top 10 feature importance for the User + Photo model.
Fig. 3
Fig. 3
Scatter plot individual Big5-Conscientiousness vs Probability of at least a comment (subject’s average).
Fig. 4
Fig. 4
Scatter plot individual Problematic Facebook Use vs Probability of at least a comment (subject’s average).
Fig. 5
Fig. 5
Effect’s plot of Conscientiousness in interaction with Problematic Facebook Use (median split). A positive association between Conscientiousness and the probability to receive a comment is strong for problematic Facebook users (i.e. higher that the median), while this association looks null for non-problematic users. See the main text for a description of how the plot is drawn.
Fig. 6
Fig. 6
Effect’s plot of Agreebleness in interaction with Problematic Facebook Use (median split). A negative trend between Agreebleness and the probability to receive a comment is markedly evident for problematic Facebook users (i.e., higher that the median), whereas this is not true for non-problematic users (i.e. lower than the median). See the main text for a description of how the plot is drawn.
See this image and copyright information in PMC

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