“…Kim and Kim, 2018; Xu and Armstrong, 2019; Yockey et al, 2019), ethnicity (e.g. Groggel et al, 2019; Szabo and Buta, 2019), age (e.g. Gewirtz-Meydan and Ayalon, 2018), religious culture (e.g.…”
We conducted a scoping review to identify and describe trends in the use of social media images as data sources to inform social science research in published articles from 2015 to 2019. The identified trends include the following: (1) there is increasing interest in social media images as research data, especially in disciplines like sociology, cultural studies, communication and environmental studies; (2) the photo sample size is often smaller than that is typically used in text-based social media analysis and usually is collected manually; (3) thematic coding, object recognition and narrative analysis are the most popular analysis methods that are often conducted manually; (4) computer vision and machine-learning technologies have been increasingly but still infrequently used and are not fit for all purposes; and (5) relatively few papers mention ethics and privacy issues, or apply strategies to address ethical issues. We identify noteworthy research gaps, and opportunities to address limitations and challenges.
“…Kim and Kim, 2018; Xu and Armstrong, 2019; Yockey et al, 2019), ethnicity (e.g. Groggel et al, 2019; Szabo and Buta, 2019), age (e.g. Gewirtz-Meydan and Ayalon, 2018), religious culture (e.g.…”
We conducted a scoping review to identify and describe trends in the use of social media images as data sources to inform social science research in published articles from 2015 to 2019. The identified trends include the following: (1) there is increasing interest in social media images as research data, especially in disciplines like sociology, cultural studies, communication and environmental studies; (2) the photo sample size is often smaller than that is typically used in text-based social media analysis and usually is collected manually; (3) thematic coding, object recognition and narrative analysis are the most popular analysis methods that are often conducted manually; (4) computer vision and machine-learning technologies have been increasingly but still infrequently used and are not fit for all purposes; and (5) relatively few papers mention ethics and privacy issues, or apply strategies to address ethical issues. We identify noteworthy research gaps, and opportunities to address limitations and challenges.
“…For instance, researchers could vary the trustworthiness and/or credibility of the source of the information communicated within the vignette, either manipulated explicitly (e.g., through expectations based on past behaviors of the character; Andrews & Rapp, 2014 ; Rapp & Gerrig, 2006 ; Sparks & Rapp, 2011 ; Wertgen et al, 2021 ) or implicitly (e.g., through demographic characteristics like race, gender, political affiliation, etc. ; Groggel et al, 2019 ; Mena et al, 2020 ; Rapp et al, 2019 ; Swire et al, 2017 ). Researchers could also modify presentations of the vignette content to offer them in different modalities or information environments (Corneille et al, 2020 ; Fazio, Dolan, & Marsh, 2015b ).…”
“…Within this study, the perceived social characteristics of Snapchat users, based on the visual presentation through avatars, could contribute to differences in how the flirtatious message and the emoji used within it are perceived. Prior scholarship has demonstrated how Twitter users' profile image influenced perceptions of trust with attractive profiles being positively associated with evaluations of trust; yet very attractive Black male and female Twitter accounts are associated with lower evaluations of trust compared to their White counterparts (Groggel et al 2019). A similar racial stereotype could be activated when participants evaluated the Snapchat conversation based on the race of characters.…”
Digital technology has long provided new ways of initiating romantic relationships as people communicate through text messages, social media, and dating applications. Emojis have been widely adopted as a means of conveying nonverbal cues in digital communication. However, what role do platform‐provided social cues, such as emojis, play in fostering or impeding clear communication and shared romantic expectations from a flirtatious text message conversation? In this study, 713 college students were randomly assigned to read a Snapchat conversation with or without emojis and, they were subsequently asked to infer the characters' thoughts and feelings, clarity of the characters' intentions, and indicate their own discomfort with receiving a similar Snapchat message. The results showed that emojis increase the clarity of the main character's intentions. Moreover, the participants' cognitive efforts, the extent to which they were emotionally affected by the conversation, and the presence of emojis reduced comfort level with receiving a similar Snapchat message. These findings suggest that emojis provide clarity to romantic conversations, which can amplify the interpersonal discomfort of receiving text‐based sexual overtures.
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