Yilang Peng
Dados Biográficos
| ID | 4270858 |
|---|---|
| NOME | Yilang Peng |
| PRENOMES | Yilang |
| SOBRENOME | Peng |
| ASSINATURA | PENG Y |
| AFILIAÇÕES | University of Georgia |
| ORCID | 0000-0001-7711-9518 |
| VERIFICADO | Sim |
| TOTAL DE OBRAS | 20 |
| TOTAL DE CITAÇÕES | 61 |
| TOTAL COMO AUTOR | 20 |
| TOTAL COMO EDITOR | 0 |
| PRIMEIRO ANO DE PUBLICAÇÃO | 2016 |
| ANO MAIS RECENTE DE PUBLICAÇÃO | 2026 |
| ÍNDICE H | 5 |
Disentangling the Three Facets of Mass Ideological Polarization
Public opinion research has intensively examined mass ideological polarization, often linking it to outcomes that threaten key democratic ideals. Both theory and empirical evidence suggest that it comprises multiple manifestations, with their relationships, however, remaining largely unclear. Meanwhile, previous studies have seldom conducted cross-contextual comparisons, which are essential for understanding how macrostructural factors shape mass…
What Makes Covid-19 Charts Understandable? A Visualization Complexity Framework for Evaluating Data Visualizations in Pandemic Media Coverage
As citizens increasingly rely on data to navigate complex scientific issues, effective visualization has become essential. This study proposes a multidimensional framework to assess visualization complexity. We collected 6,641 COVID-19 visualizations from 29 media outlets over 18 months and recruited U.S. participants ( N = 905) to evaluate a subset ( N = 640). Results indicate that traditional measures of visual complexity had limited effects on…
Connecting minds, advancing ideas, and pioneering dialogues in communication
Are partisan, unreliable, digital-born, and mass-oriented media more likely to thrive on social media? Comparing four information ecosystems
Social media platforms form information ecosystems distinct from the Web and reconfigure power relationships, especially the distribution of visibilities, among news media. We developed a theoretical framework based on structuration theory to explain the differences between the Web and social media, and investigated four prominent factors: institutional legacy, information reliability, ideological differences, and news inequalities. This study co…
How Visual Aesthetics and Calorie Density Predict Food Image Popularity on Instagram
Social media have become an important source where people are exposed to visual representations of foods. This study aims to understand what content factors contribute to the popularity of food images on Instagram. We collected 53,894 images from 90 popular food influencer accounts on Instagram over two years. Applying computer vision methods, we investigated the effects of visual aesthetics and calorie density of foods on audience engagement (i.…
A Computer Vision Methodology to Predict Brand Personality from Image Features
Using the computer vision method, this study proposes an analytical model of visual aesthetics for brand communication and analyzes the effects of visual features (i.e., colors and visual complexity) on brand personality. This study illustrates a four-step procedure correlating computationally coded visual attributes with human ratings of perceived brand personality. This study has important methodological implications for advertising researchers…
The Mobilizing Power of Visual Media Across Stages of Social-Mediated Protests
The popularity of camera phones, the availability of photo-editing apps, and the rise of visually oriented social media platforms have made it convenient for citizens to produce and circulate visual content in contentious politics. While scholars have increasingly recognized the role of visuals in mobilizing social-mediated protests, how different types of visuals affect message engagement across different stages of protests remains underexplored…
Image Clustering
Automated image analysis has received increasing attention in social scientific research, yet existing scholarship has mostly covered the application of supervised learning to classify images into predefined categories. This study focuses on the task of unsupervised image clustering, which aims to automatically discover categories from unlabelled image data. We first review the steps to perform image clustering and then focus on one key challenge…
Metrics in action
Social media metrics allow media outlets to get a granular, real-time understanding of audience preferences, and may therefore be used to decide what content to prioritize in the future. We test this mechanism in the context of Facebook, by using topic modeling and longitudinal data analysis on a large dataset comprising all posts published by major media outlets used by American citizens (N≈2.23M, 2015–2019). We find that while the overall effec…
The role of ideological dimensions in shaping acceptance of facial recognition technology and reactions to algorithm bias
Facial recognition technology has been introduced into various aspects of social life, yet it has raised concerns over its infringement of civil liberties and biases against minorities. This study investigates how three ideological dimensions—social dominance orientation, right-wing authoritarianism, and libertarianism—shape facial recognition acceptance. First, two surveys of crowdsourced workers ( N = 891 and 587) demonstrated that the acceptan…
Automated Visual Analysis for the Study of Social Media Effects
To advance our understanding of social media effects, it is crucial to incorporate the increasingly prevalent visual media into our investigation. In this article, we discuss the theoretical opportunities of automated visual analysis for the study of social media effects and present an overview of existing computational methods that can facilitate this. Specifically, we highlight the gap between the outputs of existing computer vision tools and t…
The dark side of entertainment? How viral entertaining media build an attention base for the far-right politics of The Epoch Times
To amplify their audience reach, far-right outlets need a calculated and coordinated array of acts to set the stage for audience attention and to build a communication network that spreads their messages. We examined the Facebook newsfeed history of The Epoch Times ( N = 117,274 posts from 2013 to 2020), which transitioned from a niche anti-China publication to an influential player in US far-right politics. We found that US partisan issues helpe…
An Agenda for Studying Credibility Perceptions of Visual Misinformation
Today's political misinformation has increasingly been created and consumed in visual formats, such as photographs, memes, and videos. Despite the ubiquity of visual media and the growing scholarly attention to misinformation, there is a relative dearth of research on visual misinformation. It remains unclear which specific visual formats (e.g., memes, visualizations) and features (e.g., color, human faces) contribute to visual misinformation's i…
The Importance of Trending Topics in the Gatekeeping of Social Media News Engagement
Digital gatekeepers have greatly shaped the gatekeeping process of news consumption and news engagement, but how digital gatekeepers work is understudied. This study focuses on one example of digital gatekeepers, trending topics on social media, which aggregate the most popular search terms and present them to the public. We utilize a natural experiment on Weibo by analyzing user engagement data of 36,239 posts in three consecutive weeks, during …
Fitspiration on Instagram
This study aimed to examine features of objectified images in popular fitspiration accounts on social media, identify the most prevalent user discussion topics about these images, and investigate the linkages between specific objectification cues and discussion topics. We employed content analysis to identify gender-specific objectification elements in fitspiration images (N = 2,000) on Instagram and unsupervised topic modeling to uncover topics …
Anatomy of audience duplication networks
While partisan selective exposure could drive audience fragmentation, other individual factors might also differentiate news diets. This study applies a method that disentangles the differential contributions of the individual characteristics to audience duplication networks. By analyzing a nationally representative survey about US adults’ media use in 2019 ( N = 12,043), we demonstrate that news fragmentation is driven by a myriad of individual …
The ideological divide in public perceptions of self-driving cars
Applications in artificial intelligence such as self-driving cars may profoundly transform our society, yet emerging technologies are frequently faced with suspicion or even hostility. Meanwhile, public opinions about scientific issues are increasingly polarized along the ideological line. By analyzing a nationally representative panel in the United States, we reveal an emerging ideological divide in public reactions to self-driving cars. Compare…
What Makes Politicians’ Instagram Posts Popular? Analyzing Social Media Strategies of Candidates and Office Holders with Computer Vision
Previous research on the success of politicians’ messages on social media has so far focused on a limited number of platforms, especially Facebook and Twitter, and predominately studied the effects of textual content. This research reported here applies computer vision analysis to a total of 59,020 image posts published by 172 Instagram accounts of U.S. politicians, both candidates and office holders, and examines how visual attributes influence …
Same Candidates, Different Faces
How do today’s partisan media outlets produce ideological bias in their visual coverage of political candidates? Applying computer vision techniques, this study examined 13,026 images from 15 news websites about the two candidates in the 2016 U.S. presidential election. The analysis unveils a set of visual attributes (e.g., facial expressions, face size, skin condition) that were adopted by media outlets of varying ideologies to differentially po…
The influence of weight-of-evidence strategies on audience perceptions of (un)certainty when media cover contested science
Controversy in science news accounts attracts audiences and draws attention to important science issues. But sometimes covering multiple sides of a science issue does the audience a disservice. Counterbalancing a truth claim backed by strong scientific support with a poorly backed argument can unnecessarily heighten audience perceptions of uncertainty. At the same time, journalistic norms often constrain reporters to “get both sides of the story”…
An Agenda for Studying Credibility Perceptions of Visual Misinformation
Today's political misinformation has increasingly been created and consumed in visual formats, such as photographs, memes, and videos. Despite the ubiquity of visual media and the growing scholarly attention to misinformation, there is a relative dearth of research on visual misinformation. It remains unclear which specific visual formats (e.g., memes, visualizations) and features (e.g., color, human faces) contribute to visual misinformation's i…
What Makes Politicians’ Instagram Posts Popular? Analyzing Social Media Strategies of Candidates and Office Holders with Computer Vision
Previous research on the success of politicians’ messages on social media has so far focused on a limited number of platforms, especially Facebook and Twitter, and predominately studied the effects of textual content. This research reported here applies computer vision analysis to a total of 59,020 image posts published by 172 Instagram accounts of U.S. politicians, both candidates and office holders, and examines how visual attributes influence …
The Mobilizing Power of Visual Media Across Stages of Social-Mediated Protests
The popularity of camera phones, the availability of photo-editing apps, and the rise of visually oriented social media platforms have made it convenient for citizens to produce and circulate visual content in contentious politics. While scholars have increasingly recognized the role of visuals in mobilizing social-mediated protests, how different types of visuals affect message engagement across different stages of protests remains underexplored…
Automated Visual Analysis for the Study of Social Media Effects
To advance our understanding of social media effects, it is crucial to incorporate the increasingly prevalent visual media into our investigation. In this article, we discuss the theoretical opportunities of automated visual analysis for the study of social media effects and present an overview of existing computational methods that can facilitate this. Specifically, we highlight the gap between the outputs of existing computer vision tools and t…
Image Clustering
Automated image analysis has received increasing attention in social scientific research, yet existing scholarship has mostly covered the application of supervised learning to classify images into predefined categories. This study focuses on the task of unsupervised image clustering, which aims to automatically discover categories from unlabelled image data. We first review the steps to perform image clustering and then focus on one key challenge…
Anatomy of audience duplication networks
While partisan selective exposure could drive audience fragmentation, other individual factors might also differentiate news diets. This study applies a method that disentangles the differential contributions of the individual characteristics to audience duplication networks. By analyzing a nationally representative survey about US adults’ media use in 2019 ( N = 12,043), we demonstrate that news fragmentation is driven by a myriad of individual …
The dark side of entertainment? How viral entertaining media build an attention base for the far-right politics of The Epoch Times
To amplify their audience reach, far-right outlets need a calculated and coordinated array of acts to set the stage for audience attention and to build a communication network that spreads their messages. We examined the Facebook newsfeed history of The Epoch Times ( N = 117,274 posts from 2013 to 2020), which transitioned from a niche anti-China publication to an influential player in US far-right politics. We found that US partisan issues helpe…
The influence of weight-of-evidence strategies on audience perceptions of (un)certainty when media cover contested science
Controversy in science news accounts attracts audiences and draws attention to important science issues. But sometimes covering multiple sides of a science issue does the audience a disservice. Counterbalancing a truth claim backed by strong scientific support with a poorly backed argument can unnecessarily heighten audience perceptions of uncertainty. At the same time, journalistic norms often constrain reporters to “get both sides of the story”…
Same Candidates, Different Faces
How do today’s partisan media outlets produce ideological bias in their visual coverage of political candidates? Applying computer vision techniques, this study examined 13,026 images from 15 news websites about the two candidates in the 2016 U.S. presidential election. The analysis unveils a set of visual attributes (e.g., facial expressions, face size, skin condition) that were adopted by media outlets of varying ideologies to differentially po…
The ideological divide in public perceptions of self-driving cars
Applications in artificial intelligence such as self-driving cars may profoundly transform our society, yet emerging technologies are frequently faced with suspicion or even hostility. Meanwhile, public opinions about scientific issues are increasingly polarized along the ideological line. By analyzing a nationally representative panel in the United States, we reveal an emerging ideological divide in public reactions to self-driving cars. Compare…
What Makes Politicians’ Instagram Posts Popular? Analyzing Social Media Strategies of Candidates and Office Holders with Computer Vision
Previous research on the success of politicians’ messages on social media has so far focused on a limited number of platforms, especially Facebook and Twitter, and predominately studied the effects of textual content. This research reported here applies computer vision analysis to a total of 59,020 image posts published by 172 Instagram accounts of U.S. politicians, both candidates and office holders, and examines how visual attributes influence …
Fitspiration on Instagram
This study aimed to examine features of objectified images in popular fitspiration accounts on social media, identify the most prevalent user discussion topics about these images, and investigate the linkages between specific objectification cues and discussion topics. We employed content analysis to identify gender-specific objectification elements in fitspiration images (N = 2,000) on Instagram and unsupervised topic modeling to uncover topics …
Anatomy of audience duplication networks
While partisan selective exposure could drive audience fragmentation, other individual factors might also differentiate news diets. This study applies a method that disentangles the differential contributions of the individual characteristics to audience duplication networks. By analyzing a nationally representative survey about US adults’ media use in 2019 ( N = 12,043), we demonstrate that news fragmentation is driven by a myriad of individual …
The Importance of Trending Topics in the Gatekeeping of Social Media News Engagement
Digital gatekeepers have greatly shaped the gatekeeping process of news consumption and news engagement, but how digital gatekeepers work is understudied. This study focuses on one example of digital gatekeepers, trending topics on social media, which aggregate the most popular search terms and present them to the public. We utilize a natural experiment on Weibo by analyzing user engagement data of 36,239 posts in three consecutive weeks, during …
Metrics in action
Social media metrics allow media outlets to get a granular, real-time understanding of audience preferences, and may therefore be used to decide what content to prioritize in the future. We test this mechanism in the context of Facebook, by using topic modeling and longitudinal data analysis on a large dataset comprising all posts published by major media outlets used by American citizens (N≈2.23M, 2015–2019). We find that while the overall effec…
The role of ideological dimensions in shaping acceptance of facial recognition technology and reactions to algorithm bias
Facial recognition technology has been introduced into various aspects of social life, yet it has raised concerns over its infringement of civil liberties and biases against minorities. This study investigates how three ideological dimensions—social dominance orientation, right-wing authoritarianism, and libertarianism—shape facial recognition acceptance. First, two surveys of crowdsourced workers ( N = 891 and 587) demonstrated that the acceptan…
Automated Visual Analysis for the Study of Social Media Effects
To advance our understanding of social media effects, it is crucial to incorporate the increasingly prevalent visual media into our investigation. In this article, we discuss the theoretical opportunities of automated visual analysis for the study of social media effects and present an overview of existing computational methods that can facilitate this. Specifically, we highlight the gap between the outputs of existing computer vision tools and t…
The dark side of entertainment? How viral entertaining media build an attention base for the far-right politics of The Epoch Times
To amplify their audience reach, far-right outlets need a calculated and coordinated array of acts to set the stage for audience attention and to build a communication network that spreads their messages. We examined the Facebook newsfeed history of The Epoch Times ( N = 117,274 posts from 2013 to 2020), which transitioned from a niche anti-China publication to an influential player in US far-right politics. We found that US partisan issues helpe…
An Agenda for Studying Credibility Perceptions of Visual Misinformation
Today's political misinformation has increasingly been created and consumed in visual formats, such as photographs, memes, and videos. Despite the ubiquity of visual media and the growing scholarly attention to misinformation, there is a relative dearth of research on visual misinformation. It remains unclear which specific visual formats (e.g., memes, visualizations) and features (e.g., color, human faces) contribute to visual misinformation's i…
How Visual Aesthetics and Calorie Density Predict Food Image Popularity on Instagram
Social media have become an important source where people are exposed to visual representations of foods. This study aims to understand what content factors contribute to the popularity of food images on Instagram. We collected 53,894 images from 90 popular food influencer accounts on Instagram over two years. Applying computer vision methods, we investigated the effects of visual aesthetics and calorie density of foods on audience engagement (i.…
A Computer Vision Methodology to Predict Brand Personality from Image Features
Using the computer vision method, this study proposes an analytical model of visual aesthetics for brand communication and analyzes the effects of visual features (i.e., colors and visual complexity) on brand personality. This study illustrates a four-step procedure correlating computationally coded visual attributes with human ratings of perceived brand personality. This study has important methodological implications for advertising researchers…
The Mobilizing Power of Visual Media Across Stages of Social-Mediated Protests
The popularity of camera phones, the availability of photo-editing apps, and the rise of visually oriented social media platforms have made it convenient for citizens to produce and circulate visual content in contentious politics. While scholars have increasingly recognized the role of visuals in mobilizing social-mediated protests, how different types of visuals affect message engagement across different stages of protests remains underexplored…
Image Clustering
Automated image analysis has received increasing attention in social scientific research, yet existing scholarship has mostly covered the application of supervised learning to classify images into predefined categories. This study focuses on the task of unsupervised image clustering, which aims to automatically discover categories from unlabelled image data. We first review the steps to perform image clustering and then focus on one key challenge…
Are partisan, unreliable, digital-born, and mass-oriented media more likely to thrive on social media? Comparing four information ecosystems
Social media platforms form information ecosystems distinct from the Web and reconfigure power relationships, especially the distribution of visibilities, among news media. We developed a theoretical framework based on structuration theory to explain the differences between the Web and social media, and investigated four prominent factors: institutional legacy, information reliability, ideological differences, and news inequalities. This study co…
Disentangling the Three Facets of Mass Ideological Polarization
Public opinion research has intensively examined mass ideological polarization, often linking it to outcomes that threaten key democratic ideals. Both theory and empirical evidence suggest that it comprises multiple manifestations, with their relationships, however, remaining largely unclear. Meanwhile, previous studies have seldom conducted cross-contextual comparisons, which are essential for understanding how macrostructural factors shape mass…
What Makes Covid-19 Charts Understandable? A Visualization Complexity Framework for Evaluating Data Visualizations in Pandemic Media Coverage
As citizens increasingly rely on data to navigate complex scientific issues, effective visualization has become essential. This study proposes a multidimensional framework to assess visualization complexity. We collected 6,641 COVID-19 visualizations from 29 media outlets over 18 months and recruited U.S. participants ( N = 905) to evaluate a subset ( N = 640). Results indicate that traditional measures of visual complexity had limited effects on…
Connecting minds, advancing ideas, and pioneering dialogues in communication
Political science (12 obras) · Computer Science (11 obras) · Psychology (11 obras) · Social Psychology (10 obras) · Sociology (10 obras) · Advertising (8 obras) · Politics (8 obras) · Social Media and Politics (8 obras) · Business (7 obras) · Media Studies and Communication (7 obras)