2021
DOI: 10.1177/14614448211026580
|View full text |Cite
|
Sign up to set email alerts
|

Guy next door and implausibly attractive young women: The visual frames of social media propaganda

Abstract: This study combines data analysis with multilevel processing of visual communication to classify the visual frames of state-sponsored social media propaganda. We relied on Twitter’s Election Integrity data to sample five propaganda targets of the Internet Research Agency, including Russian and American partisan groups, and explored how their operations deviated from canonical state propaganda marked by symbols of national identity and heroic masculinity. The results show that the visual frames employed by the … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
1
1

Citation Types

0
0
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
4
2

Relationship

0
6

Authors

Journals

citations
Cited by 8 publications
(5 citation statements)
references
References 73 publications
0
0
0
Order By: Relevance
“…As gender identity is largely fluid and constructed, and ultimately influenced by intersectionality with race, class, and age (Cole, 2009;Crenshaw, 1997), it presents a formidable challenge for tools like the DeepFace, which often struggles with gender classification due to the dynamic nature of such identities, particularly for visuals that do not conform to the traditional gender binary or whose appearance has been altered by camera filters. This is in line with previous research that cautioned against gender classification based on facial structures without taking into account the contextual and social aspects of one's identity (Bastos et al, 2023). A more comprehensive model would analyze not just facial features, but also user-generated content like bios and self-reported names to portray the array of complex elements shaping one's gender identity on social platforms.…”
Section: Discussionsupporting
confidence: 82%
See 4 more Smart Citations
“…As gender identity is largely fluid and constructed, and ultimately influenced by intersectionality with race, class, and age (Cole, 2009;Crenshaw, 1997), it presents a formidable challenge for tools like the DeepFace, which often struggles with gender classification due to the dynamic nature of such identities, particularly for visuals that do not conform to the traditional gender binary or whose appearance has been altered by camera filters. This is in line with previous research that cautioned against gender classification based on facial structures without taking into account the contextual and social aspects of one's identity (Bastos et al, 2023). A more comprehensive model would analyze not just facial features, but also user-generated content like bios and self-reported names to portray the array of complex elements shaping one's gender identity on social platforms.…”
Section: Discussionsupporting
confidence: 82%
“…Previous research has charted how the visual strategies employed by the IRA displayed cultural insight and familiarity with the social identity of their targets by trafficking in the tropes of ordinary urban men and attractive young women. This body of work identified differences across campaign targets, with males more likely to appear in the BlackLivesMatter and Russian targeted groups, and females dominating the profile composition of Christians, Conservatives, and particularly Trump supporters (Bastos et al, 2023;Freelon et al, 2020). BlackLivesMatter activists and Christians were more likely to be depicted with high angles, whereas low angles prevailed among Trump supporters and Russian groups.…”
Section: Previous Workmentioning
confidence: 98%
See 3 more Smart Citations