In this short paper, we describe an application of data mining techniques to predict Instagram users\u2019 addiction from a set of features related to (i) Instagramc aptions extracted from photos, videos, comments, and stories, and Instagram indicators such as number of followers and following, blocked and closed friends,and frequency of use. We first applied text mining to explore and describe the main contents of Instagram captions. Next, we used a set of non parametric models and ensemble methods to predict Instagram addiction as measured by the Instagram ad-diction scale [1]. Models were compared via cross-validation using test and training (random) sets from the original dataset. Results showed that Instagram addiction is mainly predicted by the overall time spent on Instagram, writing stories and comments, and number of followers. Moreover, the results suggest that Instagram users made use of photos/videos and stories/comments differently, with the latter being mostly related to emoticons, experiences, and relationships with other users

Calcagni', Antonio

Canale, Natale

Cortellazzo, Veronica

Girardi, Paolo

Guizzo, Francesca

In this short paper, we describe an application of data mining techniques to predict Instagram users’ addiction from a set of features related to (i) Instagramc aptions extracted from photos, videos, comments, and stories, and Instagram indicators such as number of followers and following, blocked and closed friends,and frequency of use. We first applied text mining to explore and describe the main contents of Instagram captions. Next, we used a set of non parametric models and ensemble methods to predict Instagram addiction as measured by the Instagram ad-diction scale [1]. Models were compared via cross-validation using test and training (random) sets from the original dataset. Results showed that Instagram addiction is mainly predicted by the overall time spent on Instagram, writing stories and comments, and number of followers. Moreover, the results suggest that Instagram users made use of photos/videos and stories/comments differently, with the latter being mostly related to emoticons, experiences, and relationships with other users

Antonio Calcagnì

Veronica Cortellazzo

Francesca Guizzo

Paolo Girardi

Natale Canale

Archivio istituzionale della ricerca - Università di Padova

Original Citation:Predicting social media addiction fromInstagram profiles: A data mining approachPearsonPublisher:Published version:DOI:Terms of use:Open Access(Article begins on next page)This article is made available under terms and conditions applicable to Open Access Guidelines, asdescribed at http://www.unipd.it/download/file/fid/55401 (Italian only)Availability:This version is available at: 11577/3355408 since: 2020-10-30T16:05:40ZUniversità degli Studi di PadovaPadua Research Archive - Institutional RepositoryPredicting social media addiction fromInstagram profiles: A data mining approachUso del data mining per predire la dipendenza daInstagram in un campione italiano di studentiAntonio Calcagnı̀, Veronica Cortellazzo, Francesca Guizzo, Paolo Girardi, NataleCanaleAbstract In this short paper, we describe an application of data mining techniquesto predict Instagram users’ addiction from a set of features related to (i) Instagramcaptions extracted from photos, videos, comments, and stories, and Instagram in-dicators such as number of followers and following, blocked and closed friends,and frequency of use. We first applied text mining to explore and describe the maincontents of Instagram captions. Next, we used a set of non parametric models andensemble methods to predict Instagram addiction as measured by the Instagram ad-diction scale [1]. Models were compared via cross-validation using test and training(random) sets from the original dataset. Results showed that Instagram addiction ismainly predicted by the overall time spent on Instagram, writing stories and com-ments, and number of followers. Moreover, the results suggest that Instagram usersmade use of photos/videos and stories/comments differently, with the latter beingmostly related to emoticons, experiences, and relationships with other users.Abstract Questo lavoro presenta alcuni risultati di una ricerca più ampia condottasu un campione di giovani donne italiane circa l’utilizzo di Instagram come fa-cilitatore dell’oggetivizzazione sessuale. In questo lavoro si presentano i risultatirelativi allo studio circa il modo con cui attività di Instagram come pubblicazionidi foto, video, commenti e realizzazione di storie possano predire la dipendenzadal social media, come misurato dall’apposita scala di Instagram addiction. TaleAntonio Calcagnı̀,University of Padova, e-mail: antonio.calcagni@unipd.itVeronica Cortellazzo,University of Padova, e-mail: veronica.cortellazzo@studenti.unipd.itFrancesca Guizzo,University of Padova, e-mail: francesca.guizzo@unipd.itPaolo Girardi,University of Padova, e-mail: paolo.girardi@unipd.itNatale Canale,University of Padova, e-mail: natale.canale@unipd.it12analisi è stata realizzata mediante data mining, utilizzando una serie di modelli nonparametrici e metodi di ensemble. La validazione e la scelta del modello migliore èstata effettuata via validazione incrociata. I risultati mettono in evidenza il ruolo deltempo trascorso su Instagram, del numero di storie, commenti e followers come el-ementi di predizione della dipendenza dal social network. I risultati, inoltre, hannoaltresı̀ evidenziato come gli utenti tendano ad utilizzare video e foto in maniera di-versa da commenti e storie: questi ultimi, infatti, sembrano maggiormente connessia esperienze, emozioni e relazioni rispetto ai primi.Key words: data mining, text mining, Instagram, social media addiction1 IntroductionInstagram is a well-known social platform commonly used for personal reasonsas well as business activities. Over the last years, it has gained wide popularityacross the globe, becoming one of the most popular photo-sharing applications onthe Facebook platform [2]. In particular, recent trends show that Instagram is themost important network being used among adolescents [3]. Recently, a number ofresearch have shown the role of Instagram in several psychological processes, suchas women objectification (e.g., see [4]). In this respect, appearance-related com-ments on women’s bodies accompanying Instagram images seem to play a role inbody dissatisfaction as well as women self-objectification [5]. This and other resultssuggest the importance of investigating the interplay between social media behav-iors and social media addiction, especially in young users [6].In this short paper we will focus on Instagram addiction in a sample of Italianstudents. In particular, we will investigate the predictive role of Instagram contentssuch as text (comments, hashtags, photo captions/descriptions), indicators (numberof followers/followings, blocked users, closed friends), and activity frequency onsocial media addiction, as measured by the Instagram Addiction Scale [1]. Data min-ing techniques have been used to analyse the data. Particularly, text mining was ap-plied to textual components of the dataset (comments, emoticons, captions) whereasa set of non parametric models and ensemble methods has been used to choose thebest model and predictors for the response variable Instagram addiction [7]. Theresults suggest that the latter is mainly predicted by activities like interactions withother users of the social networking system via messages, comments, and likes.Predicting social media addiction from Instagram profiles: A data mining approach 32 Data and Methods2.1 DataThe Instagram Addiction Scale [1] was administered to N = 97 female participants,all of them using Instagram on a daily basis. Data were collected by using an onlinesurvey.1 Subjects were between 18 and 31 years old with an average age of 23.64years (standard deviation 2.23). They were asked to answer fifteen items groupedinto two sub-scales (i.e., Social effect, Compulsion) using an ordinal scale with sixanchors. As scales were standardized, final scores were computed by summing theitems corresponding to each sub-scale. Thus, the aggregated variable addiction wasthen defined, with higher scores being indicative of higher levels of Instagram ad-diction. For each participant, all Instagram data were also available in compressedformat (up to six months before). They consisted in comments to other posts, con-nections with other users (followers, following, friends, blocked users), likes givento media (photos and videos) as well as comments, media (photo, stories, videos),searchers (texts, hashtags), and stories. For all these data, temporal informationregarding the use of the social network (time, day) were also available. The finaldataset comprises 94 subjects and 95 variables, including the response variable re-garding Instagram addiction. Before running the analyses, 3 subjects were excludedas they included missing observations (row-wise exclusion) whereas 36 variableswere left out from the analyses because of multicollinearity (as indicated by thethreshold r ≥ 0.9).2.2 MethodsData were analysed by means of data mining methods. With regards to the tex-tual part of the data (photo captions, comments, hashtags), text mining descriptivetechniques (i.e., most frequent terms, bigrams, graphical analyses, sentiment analy-sis) were used as implemented by the R library TextWiller [8] and tidytext[9]. All texts from Instagram were pre-processed according to standard text-miningpre-processing procedure (i.e., text-normalization, stopwords elimination, tokeniza-tion) [10]. In order to predict Instagram addiction with respect to 95 features ofInstagram use, the following non-parametric and ensemble methods were insteadadopted: (i) Principal component regression (tuning: number of variables), (ii) Par-tial least squares regression (tuning: number of variables), (iii) Lasso regression(tuning: λ penalty parameter), (iv) Regression trees (tuning: α complexity parame-ter), (v) Random forest (tuning: number of variables), (vi) Boosting (tuning: number1 The institutional review board at the University of Padova gave ethical approval for the study(protocol number: 2956)4and depth of trees), (vii) Bagging. They were compared via 10-fold cross validation,with prediction errors being computed via Out Of Bag (OOB) approach.23 Results and DiscussionFigure 1 shows wordclouds of the most frequent terms as extracted from captionsof Instagram photos, videos, comments, and stories. Results indicate how distri-butions of photos/videos’ comments were different from those of stories: indeed,words used in the first category were essentially descriptive (i.e., they just de-scribed photos/videos in terms of reference to places or experiences like traveling)whereas words used in stories/comments contained mainly emoticons and tag tousers. Therefore, Instagram users made use of stories/comments and photos/videosdifferently. In general, sentiment analyses of captions revealed the presence of neu-tral sentiments more frequently than negative and positive sentiments. In particular,comments and stories showed a positive sentiment when compared to photos andvideos, which suggest that users tended to use photos/videos and comments/storieswith a different strategy.(a) Photos and videos (b) Comments (c) StoriesFig. 1: Wordcloud for captions of Photos, videos, comments, and stories.In order to select the best subset of predictors of Instagram addiction from thelarge set containing 95 features, we proceeded by running a set of non-parametric2 More in details, ten train/test sets were created by bootstrapping the original dataset row-wise.Then, models were estimated for each training data using a 10-fold cross validation; cv-errors werecomputed and used to select the optimal tuned model. The best model was then chosen among thosehaving minimum RMSE, which was computed on the test sets using the out-of-bag observations.Finally, the prediction error of the model was obtained as average of the ten RMSEs. Note thatcross-validation was used as implemented in the caret library [11].Predicting social media addiction from Instagram profiles: A data mining approach 5models and ensemble methods as describe in section 2.2. Table 1 show the resultsof model comparison on training and test sets, respectively. Among others, Randomforest achieved lower MAE and RMSE w.r.t. parameters estimation (training set)and, in a similar way, it showed lower RMSE in the set as well. Thus, Randomforest was selected as the best predictive model of Instagram addiction.RMSE Test set errorPCA regression 0.7769 0.8715PLS regression 0.663 1.0411LASSO regression 0.7189 1.0016Regression Tree 0.7387 0.8615Bagging 0.6017 0.7631Random Forest 0.6403 0.7324Boosting 0.6749 0.7909Table 1: Model comparison on test set. Note that Test set error is computed viabootstrap prediction error using 10 samples whereas the best model is representedin gray tones.Fig. 2: Random forest to predict Instagram addiction: (A) RMSE as a function ofthe best number of predictors, (B) Variable importance plot for the final model. Notethat variable importance measures are computed via the importance() functionas implemented in the randomforests library [12].The Random forest model was built on 500 trees which selected a final set of 67predictors. Figure 2 shows the results of the final model. Overall, they suggested thefollowing main predictors for Instagram addiction (in order of importance): Over-6all time spent on Instagram (Tempo giorno insta), replies to users’ stories onSaturday (Sab SAct), content searches during the night (Notte R), writing com-ments overall (num Commenti) or in a specific temporal window, namely on after-noon (Pom cC) and on Wednesday (Mer C), number of followers (Followers).4 ConclusionWe investigated the role of Instagram user’s data (captions, photos, comments, likes,stories, hashtags, following and followers) in predicting social media addiction asmeasured by Instagram Addiction Scale [1] in a sample of Italian students. We useddata mining techniques in order to (i) describe the use of Instagram commonly madeby users and (ii) find predictors of Instagram addiction from a large database con-taining 95 features. The results suggested the importance of some variables, suchas time spent on Instagram, writing stories, comments, and number of followers, aspredictors of Instagram addiction. Moreover, the results also showed that users tendto use photos/videos and stories/comments differently: the first were mainly adoptedfor describing the objects they are related to. By contrast, the latter are more thansimple descriptions, as they include emoticons, reference to places and experiences,tags to other users. Further studies need to be conducted to establish insights into thevarious mechanisms of Instagram addiction. For instance, text mining results (e.g.,emoticons, sentiments) may be used to define new predictors of social media addic-tion, which would shed light on the role played by typed emotions on this emergingphenomena.References1. K. Kircaburun, M.D. Griffiths, Journal of behavioral addictions 7(1), 158 (2018)2. Z. Li, A. Agarwal, Management Science 63(10), 3438 (2017)3. R.C. Brown, T. Fischer, A.D. Goldwich, F. Keller, R. Young, P.L. Plener, Psychologicalmedicine 48(2), 337 (2018)4. E.P. Meier, J. Gray, Cyberpsychology, Behavior, and Social Networking 17(4), 199 (2014)5. M. Tiggemann, I. Barbato, Body image 27, 61 (2018)6. P. Wallace, EMBO reports 15(1), 12 (2014)7. A. Azzalini, B. Scarpa, Analisi dei dati e data mining (Springer Science & Business Media,2009)8. D. Solari, A. Sciandra, L. Finos, Journal of Open Source Software 4(41), 1256 (2019). DOI10.21105/joss.01256. URL https://doi.org/10.21105/joss.012569. J. Silge, D. Robinson, Journal of Open Source Software 1(3), 37 (2016)10. J. Silge, D. Robinson, Text mining with R: A tidy approach (” O’Reilly Media, Inc.”, 2017)11. M. Kuhn, et al., Journal of statistical software 28(5), 1 (2008)12. A. Liaw, M. Wiener, R News 2(3), 18 (2002). URL https://CRAN.R-project.org/doc/Rnews/

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Predicting social media addiction fromInstagram profiles: A data mining approach

Predicting social media addiction fromInstagram profiles: A data mining approach

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