Selected Papers of #AoIR2020:  

The 22nd Annual Conference of the  
Association of Internet Researchers 

Philadelphia, PA, USA / 13-16 Oct 2021  
 

 

 
Bastos, M.; Mercea, D.; Gouveia, F. (2021, October). Guy Next Door and Implausibly Attractive Young 
Women: The Visual Frames of Social Media Propaganda. Paper presented at AoIR 2021: The 22nd 
Annual Conference of the Association of Internet Researchers. Philadelphia, PA, USA: AoIR. Retrieved 
from http://spir.aoir.org. 
 

GUY NEXT DOOR AND IMPLAUSIBLY ATTRACTIVE YOUNG WOMEN: 
THE VISUAL FRAMES OF SOCIAL MEDIA PROPAGANDA 
 
Marco Bastos 
University College Dublin, Ireland 
 
Dan Mercea 
City, University of London, United Kingdom 
 
Fabio Goveia 
Federal University of Espírito Santo, Brazil 
 
Extended abstract 
The research question driving this study is that tropes of heroic resistance or military 
endurance, typical of canonical state propaganda, track poorly in a context of 
information but not attrition warfare. We probe this question by taking stock of studies in 
visual propaganda research and testing whether social media propaganda seeks to 
embody the vox populi by employing relatable, familiar, or alternatively attractive faces 
of ordinary people―the archetypal “man on the street” (MOTS). We refer to such 
familiar or attractive faces as ‘the guy next door’ and the ‘implausibly attractive young 
woman.’ The former is embodied by an unassuming yet average male lacking overt 
tropes of hegemonic masculinity like strength and dominance commonly embraced by 
Russian propaganda (Romanets, 2017), while the latter is characterized by tangible 
exploitation of the female body for maximum erotic impact and objectification (Davis, 
2018). 
 
To this end, we analyzed the visual frames in profile images released by Twitter’s 
Elections Integrity, which identified and removed the universe of false accounts 
operated by the Internet Research Agency (Elections Integrity, 2018). We sampled the 
data across five groups: Muscovites, BlackLivesMatter activists, Conservatives, Trump 
supporters, and Christians. We identified Russian accounts by querying the database 
for users that self-reported “Москва” as their location (n=206). Next, we identified 



Conservatives, Trump, Christian, and BlackLivesMatter supporters by sampling profiles 
with the keyword “Conservative” in the profile description (n=121) and repeated the 
process (without replacement) for the keywords “Trump” (n=134), “God,” “Christ,” or 
“Jesus” (n=154), and finally “Black” or “BLM” (n=79). After removing false positives and 
disambiguating profiles with multiple matches, the sampled data returned 75 
BlackLivesMatter activists, 61 Christians, 104 Conservatives, 201 Muscovites, and 57 
Trump supporter profiles. Figure 1 provides an overview of the profiled groups. 
 

  
 

  



 

 
Figure 1: From left to right, Twitter profile pictures of Trump supporters, Christians, Conservatives, 
BlackLivesMatter activists, and self-identified Muscovites. 

The multimodal information curated by IRA accounts was unpacked by image-
captioning the Twitter profile images (Martinec & Salway, 2005), so that their visual 
content was translated to text for downstream analysis. A visual inspection of the profile 
images, vis-à-vis the demographic group they targeted (Figure 1), shows that the 
semantic relationship in IRA multimodal content is of complementarity and not quite 
defined by the multimodal classes of illustration or anchorage (stat = 0). We collated the 
profile images of each of the five groups to apply pixel-level analysis that uncovers 
patterns in visual media and produces a largescale representation. We organized the 
profile images according to brightness and the color saturation of each image through 
an ImageMeasure2 plug-in for ImageJ (Rasband, 2012). Two members of the team, 
with expertise in professional photography, subsequently coded each of the images to 
identify features in the composition, framing, and photographic techniques employed to 
produce the profile image. The categories devised during the coding procedure are 
detailed in Table 1. 

 
Table 1: Coding categories applied to the Twitter profile image of Internet Research Agency accounts 
impersonating BlackLivesMatter, Christians, Conservatives, Muscovites, and Trump supporters 

In addition to explicit categories, our inductive coding rendered a key category devised 
to identify whether the profile photo explored sensual undertones, a subtle feature and 
proxy for attractiveness present in many profiles featuring women or sexualizing the 



female body. Another key category devised during the coding process refers to the 
extent to which professional photographic techniques were employed to produce the 
image. Professional photographic work generates aesthetic elements that differ 
substantively from amateur photography, notwithstanding recent advances in 
smartphone cameras, particularly the expert use of hue, brightness, and light controls. 
We refer to this category as Quality to distinguish between professional-looking photos 
from those that are discernibly produced by users themselves. Finally, we triangulated 
the qualitative analysis with computer-assistant image processing using the bwimage R 
package (Biagolini-Jr & Macedo, 2019) to identify patterns in the groups of profile 
images.  
 
Results 
The IRA propaganda campaign resorted to ordinary individuals but unordinary attractive 
young women, often featured with professional makeup in professional-shot photos 
against a soft, balanced lighting. The chromatic and compositional variance identified in 
the profile photos show that US conservative groups are targeted with a composite of 
average young men and sensually crafted images of young women typical of soft 
advertisements employed by the cosmetic industry. The profile images in the Russians 
cohort, on the other hand, are marked by tropes of virility and domination. Emphasis is 
shifted from ordinary-looking male individuals to compositional undertones emphasizing 
power and adventure. BlackLivesMatter activists are framed with another compositional 
frame altogether; depicted largely with high angles projecting tense expressions in a 
single headshot. These compositional choices do not merely depict gender inequalities 
and gendered stereotypes but celebrate them. 
 
The results are also consistent with the perceived notion that state-sponsored 
propaganda on social media exploits existing social tensions by framing contentious 
issues in a familiar but partisan light. IRA activity expertly pitched to Right-wing framings 
and beliefs, but the agency also targeted distinct sections of the US electorate, including 
liberal subgroups, chief of which were BlackLivesMatter activists. Religious, racial, and 
cultural groups are targeted with disguised social media accounts exploring biases and 
prejudices, but also perceived markers of membership of these groups. The multitude of 
targeted social groups, and the broad ideological spectrum of their interests, suggest 
that the target of the propaganda efforts is not the social groups the campaign chooses 
to personify, but social cohesion itself. The emphasis and perhaps exaggeration placed 
on existing social divisions is leveraged to amplify threats and ultimately erode social 
trust and social cohesion. 
 
In the end, the guy next door represents a considerable departure from archetypes of 
heroic masculinity; indeed, the guy next door is the embodiment of unassuming, 
dependable, yet average masculinity. He is not just ordinary, but also local, thus 
submitting a sense of familiarity that effortlessly evolves to notions of reliability and 
trustworthiness. The visual trope of the affable guy next door may conceivably ease the 
labor-intensive costs of infiltration. Similarly, the cohort of implausibly attractive young 
women may follow a similar tactic of infiltration and subversion that exploits the male 
gaze and the depiction of women as sexual objects for the pleasure of the male viewer. 
However implausibly attractive they may be, this visual trope is seemingly an optimal 
candidate for becoming embedded with Trump supporters. 



 
References 
Biagolini-Jr, C., & Macedo, R. H. (2019). bwimage: A package to describe image 

patterns in natural structures. F1000Research, 8(1168), 1168.  
Davis, S. E. (2018). Objectification, Sexualization, and Misrepresentation: Social Media 

and the College Experience. Social Media + Society, 4(3), 2056305118786727. 
doi: 10.1177/2056305118786727 

Elections Integrity. (2018). Data archive. from Twitter, Inc. 
https://about.twitter.com/en_us/values/elections-integrity.html 

Martinec, R., & Salway, A. (2005). A system for image–text relations in new (and old) 
media. Visual communication, 4(3), 337-371.  

Rasband, W. (2012). ImageJ: Image processing and analysis in Java. Astrophysics 
Source Code Library.  

Romanets, M. (2017). Virtual warfare: Masculinity, sexuality, and propaganda in the 
Russo-Ukrainian war. East/West: Journal of Ukrainian Studies, 4(1), 159-177.  

Seo, H. (2019). Visual Propaganda and Social Media. The SAGE Handbook of 
Propaganda, 126.  

 

https://about.twitter.com/en_us/values/elections-integrity.html