Using Big Data as a window into consumers’ psychology

Matz, Sandra; Netzer, Oded

doi:10.1016/j.cobeha.2017.05.009

Cited by 130 publications

(107 citation statements)

References 44 publications

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“…Nevertheless, the paper provides input to wonder why we should limit our scope to the safety domain and not including other ones into the big data of psychology discourse. Examples of big data applications are identifiable in numerous areas of psychology, such as organizational (Guzzo et al, 2015;Tonidandel et al, 2016), educational (aka learning analytics; Watson and Christensen, 2017;Maldonado-Mahauad et al, 2018;Viberg et al, 2018;Elia et al, 2019;Shorfuzzaman et al, 2019), marketing (Hopp and Vargo, 2017;Matz and Netzer, 2017;Erceg et al, 2018;Ibrahim and Wang, 2019), personality (Bleidorn et al, 2017;Boyd and Pennebaker, 2017;Gerlach et al, 2018;Hinds and Joinson, 2019), emotion (aka affective computing; D'Mello et al, 2018; Chatterjee et al, 2019; Gruda and Hasan, 2019), psycholinguistics (Ridgeway et al, 2017;Johns, 2019;Luo et al, 2019), clinical (Anestis et al, 2016;Russ et al, 2018), cognitive (Medina and Fischer-Baum, 2017;Bhatia and Walasek, 2019), community (O'Brien, 2016), group (Guadagno et al, 2018), music (Greenberg and Rentfrow, 2017), political (Ma-Kellams et al, 2018), and positive psychology (Luhmann, 2017;Yaden et al, 2018). Additionally, the representation of BDSP as being the intersection of safety science, data science, and psychology, seems to equally fit other psychology branches.…”

Section: Big Data Of Whatever Psychologymentioning

confidence: 99%

Commentary: Principles, Approaches and Challenges of Applying Big Data in Safety Psychology Research

et al. 2019

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Section: Big Data Of Whatever Psychologymentioning

confidence: 99%

Commentary: Principles, Approaches and Challenges of Applying Big Data in Safety Psychology Research

et al. 2019

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“…OSN services and campaigners want to optimize psychological targeting. To predict OSN behavior, researchers, OSN services, and campaigners must mine for the digital footprints that are most indicative of the user personality traits that can ensure effective prediction of social influence, that is, persuasion in OSNs [25,26]. SSI is certainly one of those user personality traits.…”

Section: Introductionmentioning

confidence: 99%

Susceptibility to social influence predicts behavior on Facebook

Stöckli

Hofer

2020

PLoS ONE

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Susceptibility to social influence (SSI) has been reported as a key factor for social influence in online social networks (OSNs) such as Facebook, Instagram, and Twitter. In four online studies, we show that the personality trait of SSI, namely the susceptibility to normative influence (SNI), predicts the extent to which Facebook users comply with the behavior of others on Facebook (e.g., buying, voting, or visiting what other OSN users post). In Studies 1a and 1b, we find that SSI correlates with diverse OSN behaviors, which are the typical results of being affected by social influence. In Study 1b, we find that the perceived importance of the topic of OSN behaviors (e.g., fashion or politics) moderates the effect of SNI on OSN behavior, with a higher importance resulting in a stronger effect of SNI on OSN behavior. In Studies 2 and 3, we find that SNI predicts the extent Facebook users hypothetically "like" diverse topics on Facebook. We also find partial support for the idea that there are interactions between SNI and the Big Five personality traits (i.e., openness, conscientiousness, extraversion, agreeableness, and neuroticism) on OSN behavior. Specifically, the extent to which the Big Five personality traits of openness, agreeableness, and neuroticism predict OSN behavior depends on Facebook users' SNI. Our studies contribute to research on the personality-based prediction of OSN behavior and help in better understanding the dynamics of social influence in OSNs, underlining the vulnerability of susceptible OSN users. OPEN ACCESSCitation: Stöckli S, Hofer D (2020) Susceptibility to social influence predicts behavior on Facebook. PLoS ONE 15(3): e0229337. https://doi.org/ 10. Funding:The authors received no specific funding for this work. Competing interests:The authors have declared that no competing interests exist.to informative influence (SII), that is, one's tendency to seek information from others to reduce uncertainty in a situation [6,7]. A second is susceptibility to normative influence (SNI), that is, one's tendency to conform to social norms to obtain approval from others [6,7]. Finally, there is lack of resistance to social influence (LRSI), that is, one's lack of skepticism toward information from others and hence the tendency to undermine one's independence [8][9][10]. Having said this, a susceptible person compared with an unsusceptible person is typically more readily influenced by others-be it because of seeking information about a new product from others, because of conforming to others' expectations of what fashion style to wear, or because of not questioning others' posts about a political issue.So far, there have been only a few studies testing the idea that SSI predicts to what extent users are influenced by others in OSNs [11][12][13]. For instance, it has been found that SSI predicts who is affected by others' Facebook posts: susceptible Facebook users are affected by the content posted by all kinds of Facebook members, and unsusceptible Facebook users are affected only by "close" Fac...

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“…Many studies in various disciplines have made use of individuals' digital footprints, interpreting uploaded information and making predictions about future behaviour based on behavioural residue (e.g. De Choudhury, Counts, & Horvitz, 2013;De Choudhury, Counts, Horvitz, & Hoff, 2014;Eichstaedt et al, 2015;Matz & Netzer, 2017;Settanni & Marengo, 2015). Behavioural residue is a trail of actions or deliberately placed markers which form identity claims (Gosling, Ko, Mannarelli, & Morris, 2002, p. 381).…”

Section: Introductionmentioning

confidence: 99%

Feeling anxious? Perceiving anxiety in tweets using machine learning

Gruda

Hasan

2019

Computers in Human Behavior

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This study provides a predictive measurement tool to examine perceived anxiety from a longitudinal perspective, using a non-intrusive machine learning approach to scale human rating of anxiety in microblogs. Results suggest that our chosen machine learning approach depicts perceived user state-anxiety fluctuations over time, as well as mean trait anxiety. We further find a reverse relationship between perceived anxiety and outcomes such as social engagement and popularity. Implications on the individual, organizational, and societal levels are discussed.

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Using Big Data as a window into consumers’ psychology

Cited by 130 publications

References 44 publications

Commentary: Principles, Approaches and Challenges of Applying Big Data in Safety Psychology Research

Commentary: Principles, Approaches and Challenges of Applying Big Data in Safety Psychology Research

Susceptibility to social influence predicts behavior on Facebook

Feeling anxious? Perceiving anxiety in tweets using machine learning

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