“Likes” as social rewards: Their role in online social comparison and decisions to like other People's selfies

Pütten, Astrid M. Rosenthal‐von der; Hastall, Matthias R.; Köcher, Sören; Meske, Christian; Heinrich, Timo; Labrenz, Franziska; Ocklenburg, Sebastian

doi:10.1016/j.chb.2018.10.017

Cited by 90 publications

(64 citation statements)

References 49 publications

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“…Current results partly supported this finding by showing that there were more boys than girls who received negative attention as a form of technologyenhanced feedback. It should be studied whether technology-enhanced feedback is perceived as social acceptance from a teacher or is even comparable to "likes" in social media in the minds of pupils (Heimo et al, 2015;Rosenthal-von der Pütten, 2019).…”

Section: Technology-enhanced Feedback Connections To Learning and Acamentioning

confidence: 99%

“…Feedback provided by teachers has a connection to adopted learning goals and self-concept (Ryan & Deci, 2000a), when a pupil regards school and teachers as significant (Harter, 2012). As the feedback application is located alongside other applications in a young person's smartphone, it may be that feedback is perceived as representing social acceptance from the teacher (Heimo, Rantanen & Kimppa, 2015), even comparable with "likes" from friends in social media (Rosenthal-von der Pütten, 2019). There is a wealth of research-based information for teachers on how to implement feedback efficiently (Hattie & Timperley, 2007), at least in face-to-face situations, but less is known about technology-enhanced feedback.…”

Section: Introductionmentioning

confidence: 99%

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Technology-enhanced Feedback Profiles and their Associations with Learning and Academic Well-being Indicators in Basic Education

Oinas

Thuneberg

Vainikainen

et al. 2020

CONTEMP EDUC TECHNOL

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As a variety of commercial educational applications are currently being taken into daily use to provide technology-enhanced feedback, research is needed to observe whether pedagogical evidence of the impact of feedback on learning and well-being is being utilized. To this end, this study explores the connections between technology-enhanced feedback, motivation, competence and the relationship with teachers. A nationally representative sample of pupils undertaking Finnish basic education (N=2031) was analyzed using latent profile analysis. Seven patterns for receiving technology-enhanced feedback were identified. Most girls (80%) and boys (55%) belonged to groups receiving mainly positive feedback in the form of teacher praise, which was connected to the highest scores in all measured indicators. Although the results indicate teachers' efforts to encourage pupils through technology-enhanced feedback, we also identified profiles in which pupils (up to 30%) repeatedly received negative feedback related to behavior problems or forgotten matters, as well as profiles in which pupils (5%) reported that they never received any technology-enhanced feedback at all. Pupils who did not receive any feedback reported the lowest values in all scales. The relationship with teachers was particularly weak for pupils receiving negative feedback or no feedback. The results indicate that current technology-enhanced feedback practices do not fully meet pedagogical knowledge concerning efficient feedback.

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Section: Technology-enhanced Feedback Connections To Learning and Acamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Technology-enhanced Feedback Profiles and their Associations with Learning and Academic Well-being Indicators in Basic Education

Oinas

Thuneberg

Vainikainen

et al. 2020

CONTEMP EDUC TECHNOL

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show abstract

“…In 2018, more than three billion people spent(1), on average, several hours a day on platforms such as Instagram, Facebook, Twitter, and other more specialized forums. This pattern of social media engagement is often described as an addiction, in which people are driven to pursue positive online social feedback (2,3) to the detriment of direct social interaction and even basic needs like eating and drinking (4,5).…”

mentioning

confidence: 99%

“…Similarly, a user's social media activity increases after a post, suggestive of reward anticipation (16). In addition to its direct effect on reward, the subjective value of likes is also influenced by social comparison in a similar way as non-social rewards (3,17,18), suggesting that social rewards, just like non-social rewards (19), are relative, rather than absolute in nature. Together, these studies offer suggestive evidence that social media engagement resembles reward reinforcement.…”

mentioning

confidence: 99%

A computational reward learning account of social media engagement

Lindström¹,

Bellander²,

Chang³

et al. 2019

Preprint

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Social media has become the modern arena for human life, with billions of daily users worldwide. The intense popularity of social media is often attributed to a psychological need for social rewards (“likes”), which turns the online world into a “Skinner Box” for the modern human. Yet despite such common portrayals, empirical evidence for social media engagement as reward-based behavior remains scant. We applied a computational approach to directly test whether reward learning mechanisms contribute to social media behavior. We analyzed over one million posts from over 4,000 individuals on several social media platforms, using computational models based on reward reinforcement learning theory. Our results consistently show that human behavior on social media qualitatively and quantitatively conforms to the principles of reward learning. Results further reveal meaningful individual differences in social reward learning on social media, explained in part by variability in users’ tendency for social comparison. Together, these findings support the social reinforcement learning view of social media engagement and offer key new insights into this emergent mode of modern human behavior on an unprecedented scale.

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“…However, presently, a lot of data/content of great interest to cultural heritage applications may be also discovered within popular social media like Facebook, TripAdvisor or Twitter. A vast number of people (of any origin, language or educational profile) have accounts in (typically more than one) social media platforms, and use them to post their opinion about cultural heritage venues by publishing reviews, to describe their perceived experiences by uploading posts, to designate favorite destinations and provide useful points of interest (PoIs) by using "check-ins", or to keep up to date by subscribing to upcoming events hosted by different types of venues [71][72][73].…”

Section: The Data Acquisition Modulementioning

confidence: 99%

Hydria: An Online Data Lake for Multi-Faceted Analytics in the Cultural Heritage Domain

Deligiannis

Raftopoulou

Tryfonopoulos

et al. 2020

BDCC

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Advancements in cultural informatics have significantly influenced the way we perceive, analyze, communicate and understand culture. New data sources, such as social media, digitized cultural content, and Internet of Things (IoT) devices, have allowed us to enrich and customize the cultural experience, but at the same time have created an avalanche of new data that needs to be stored and appropriately managed in order to be of value. Although data management plays a central role in driving forward the cultural heritage domain, the solutions applied so far are fragmented, physically distributed, require specialized IT knowledge to deploy, and entail significant IT experience to operate even for trivial tasks. In this work, we present Hydria, an online data lake that allows users without any IT background to harvest, store, organize, analyze and share heterogeneous, multi-faceted cultural heritage data. Hydria provides a zero-administration, zero-cost, integrated framework that enables researchers, museum curators and other stakeholders within the cultural heritage domain to easily (i) deploy data acquisition services (like social media scrapers, focused web crawlers, dataset imports, questionnaire forms), (ii) design and manage versatile customizable data stores, (iii) share whole datasets or horizontal/vertical data shards with other stakeholders, (iv) search, filter and analyze data via an expressive yet simple-to-use graphical query engine and visualization tools, and (v) perform user management and access control operations on the stored data. To the best of our knowledge, this is the first solution in the literature that focuses on collecting, managing, analyzing, and sharing diverse, multi-faceted data in the cultural heritage domain and targets users without an IT background.

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“Likes” as social rewards: Their role in online social comparison and decisions to like other People's selfies

Cited by 90 publications

References 49 publications

Technology-enhanced Feedback Profiles and their Associations with Learning and Academic Well-being Indicators in Basic Education

Technology-enhanced Feedback Profiles and their Associations with Learning and Academic Well-being Indicators in Basic Education

A computational reward learning account of social media engagement

Hydria: An Online Data Lake for Multi-Faceted Analytics in the Cultural Heritage Domain

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