Faces engage us: photos with faces attract more likes and comments on Instagram

By Saeideh Bakhshi, David A. Shamma, Eric Gilbert. International Conference on Human Factors in Computing Systems, 2014

452 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/4eea0ae045333afdb02e709cf7b7ac99ca88cd73

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Citations in the corpus

Social media and loneliness: Why an Instagram picture may be worth more than a thousand Twitter words (2016, background): “According to a nation-wide survey, commissioned by the Mental Health Foundation, 48% of British adults believe that people in the UK are getting lonelier as time progresses, 45% report feeling lonely at least some of the time, and 42% report having felt depressed due to being alone (Griffin, 2010). ”
Characterizing the Demographics Behind the #BlackLivesMatter Movement (2015, background): “on their profile picture or name [13, 18, 9, 14], we crowdsourced these annotations.”
Quantifying Engagement with Citations on Wikipedia (2020, background): “ent is crucial for the success of Web services, and numerous researchers have focused on quantifying how Web users engage with online content, e.g., in computational advertising [6, 71], social media [5, 10, 22], or information retrieval [24, 56]. Also, while the body of work focusing on understandi”
Chic or Social: Visual Popularity Analysis in Online Fashion Networks (2014, background): “Some very recent work has looked into visual inﬂuence on popularity or behavior [2, 6, 1, 7], or in the reverse direction, to categorize visual contents using social information [8].”
Misinformation on Instagram: The Impact of Trusted Endorsements on Message Credibility (2020, background): “Instagram’s impressive growth has largely been attributed to the emphasis on image (Bakhshi et al., 2014).”
Children Seen But Not Heard: When Parents Compromise Children's Online Privacy (2015, background): “[3] found that photos with faces accrued more likes on Instagram.”
On the Value of Wikipedia as a Gateway to the Web (2021, background): “Several works have tried to predict engagement with content in social media based on social interest metrics, such as the number of post comments or likes [3, 6, 13].”
Toward Multimodal Cyberbullying Detection (2017, background): “These APIs can be used for studying diverse social, political, and information interaction issues and are being readily adopted by HCI researchers in different contexts (e.g.: [5, 20]).”
The Effect of Population and "Structural" Biases on Social Media-based Algorithms: A Case Study in Geolocation Inference Across the Urban-Rural Spectrum (2017, background): “For instance, researchers have used social media to develop new recommender systems (e.g., [38,59]), summarize the character of cities (e.g., [9,66]), and infer the location of Internet users (e.g., [34,41,45]), among other applications (e.g., [4,30,49]).”
Multilingual Visual Sentiment Concept Matching (2016, background): “in computational aesthetics [28], animated GIFs [29], and social dynamics [30].”