Despite an emergent body of literature examining refugees' use of online tools to access information, little is known about what types of information refugees encounter or engage with. Analyzing 143,201 posts and 802,173 comments on public Arabic-language Facebook pages targeting Syrian refugees from 2013 to 2018, we systematically describe one of Syrian refugees' most popular online information ecosystems. Additionally, we use engagement and comment data to develop organic measures of refugees' interactions with different information sources. We find that posts linking to official sources of information garnered more engagement than those containing unofficial information or news media content, regardless of the topic or tone of the message. Disaggregating our data over time reveals that official sources did not receive higher levels of engagement until early 2016, when new official sources created by governments and NGOs became active online and began to more consistently provide information about salient topics from asylum to sea travel. These new official sources also produced more encouraging messages relative to older official sources, perhaps heightening their appeal. By analyzing the online prevalence, content, and popularity of diverse information sources, this work contributes to our understanding of how vulnerable populations access information in the digital age, while offering policy insights to governments and NGOs seeking to disseminate information to refugees.
{"title":"#Asylum: How Syrian Refugees Engage with Online Information","authors":"Alexandra Siegel, Jessica Wolff, Jeremy Weinstein","doi":"10.51685/jqd.2024.013","DOIUrl":"https://doi.org/10.51685/jqd.2024.013","url":null,"abstract":"Despite an emergent body of literature examining refugees' use of online tools to access information, little is known about what types of information refugees encounter or engage with. Analyzing 143,201 posts and 802,173 comments on public Arabic-language Facebook pages targeting Syrian refugees from 2013 to 2018, we systematically describe one of Syrian refugees' most popular online information ecosystems. Additionally, we use engagement and comment data to develop organic measures of refugees' interactions with different information sources. We find that posts linking to official sources of information garnered more engagement than those containing unofficial information or news media content, regardless of the topic or tone of the message. Disaggregating our data over time reveals that official sources did not receive higher levels of engagement until early 2016, when new official sources created by governments and NGOs became active online and began to more consistently provide information about salient topics from asylum to sea travel. These new official sources also produced more encouraging messages relative to older official sources, perhaps heightening their appeal. By analyzing the online prevalence, content, and popularity of diverse information sources, this work contributes to our understanding of how vulnerable populations access information in the digital age, while offering policy insights to governments and NGOs seeking to disseminate information to refugees.","PeriodicalId":93587,"journal":{"name":"Journal of quantitative description: digital media","volume":"9 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141923178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fabio Votta, Simon Kruschinski, Mads Hove, Natali Helberger, T. Dobber, Claes de Vreese
Political campaigns are increasingly investing in targeted advertising on social media platforms to reach voters. Despite critical implications for citizens and elections, little is known about the targeting strategies deployed by political parties — especially in countries beyond the global north. This paper provides a comprehensive descriptive analysis of political microtargeting practices on Facebook and Instagram across 95 countries during 113 national elections. By analyzing the Meta Ad Targeting dataset, we explore targeting strategies of 54k political advertisers who ran 2.5 million ads between August 2020 and December 2022. The findings indicate that election campaigns worldwide utilize targeted advertising. Most commonly, spending is allocated towards a single targeting criterion, however, in wealthier countries and electoral systems with proportional representation, a greater amount of money is spent on microtargeting by combining multiple criteria. Furthermore, targeting strategies vary along ideological lines of political parties who seek out voters more typically aligned with each side of the political spectrum. Nonetheless, parties use microtargeting irrespective of political ideology. Our findings offer the first comparative analysis of political microtargeting on Meta platforms for countries across all continents. Methodologically, we introduce a semi-automatic method to identify worldwide political advertisers using multiple data sources. Our study deepens the understanding of how country and party contexts explain differences in targeting strategies, highlighting the need for more research beyond the global north. Finally, our results have important implications for policy makers, and other stakeholders who seek to develop regulations to address the challenges posed by political microtargeting techniques.
政治竞选活动越来越多地投资于社交媒体平台上的定向广告，以接触选民。尽管这对公民和选举有着至关重要的影响，但人们对各政党--尤其是在全球北方以外的国家--部署的定向策略知之甚少。本文对 113 次全国大选期间 95 个国家在 Facebook 和 Instagram 上的政治微定位做法进行了全面的描述性分析。通过分析 Meta Ad Targeting 数据集，我们探讨了在 2020 年 8 月至 2022 年 12 月期间投放了 250 万条广告的 54 千名政治广告商的定向策略。研究结果表明，全世界的竞选活动都在使用定向广告。然而，在较富裕的国家和实行比例代表制的选举制度中，更多的资金被用于结合多种标准的微定位。此外，各政党的目标定位策略也因意识形态的不同而各异，他们通常会寻找与政治光谱中每一方更一致的选民。不过，无论政治意识形态如何，政党都会使用微定位。我们的研究结果首次对各大洲国家 Meta 平台上的政治微目标进行了比较分析。在方法论上，我们引入了一种半自动方法，利用多种数据源识别全球政治广告商。我们的研究加深了人们对国家和政党背景如何解释定位策略差异的理解，强调了在全球北方之外开展更多研究的必要性。最后，我们的研究结果对政策制定者和其他利益相关者具有重要意义，他们需要制定相关法规来应对政治微定位技术带来的挑战。
{"title":"Who Does(n't) Target You?","authors":"Fabio Votta, Simon Kruschinski, Mads Hove, Natali Helberger, T. Dobber, Claes de Vreese","doi":"10.51685/jqd.2024.010","DOIUrl":"https://doi.org/10.51685/jqd.2024.010","url":null,"abstract":"Political campaigns are increasingly investing in targeted advertising on social media platforms to reach voters. Despite critical implications for citizens and elections, little is known about the targeting strategies deployed by political parties — especially in countries beyond the global north. This paper provides a comprehensive descriptive analysis of political microtargeting practices on Facebook and Instagram across 95 countries during 113 national elections. By analyzing the Meta Ad Targeting dataset, we explore targeting strategies of 54k political advertisers who ran 2.5 million ads between August 2020 and December 2022. The findings indicate that election campaigns worldwide utilize targeted advertising. Most commonly, spending is allocated towards a single targeting criterion, however, in wealthier countries and electoral systems with proportional representation, a greater amount of money is spent on microtargeting by combining multiple criteria. Furthermore, targeting strategies vary along ideological lines of political parties who seek out voters more typically aligned with each side of the political spectrum. Nonetheless, parties use microtargeting irrespective of political ideology. Our findings offer the first comparative analysis of political microtargeting on Meta platforms for countries across all continents. Methodologically, we introduce a semi-automatic method to identify worldwide political advertisers using multiple data sources. Our study deepens the understanding of how country and party contexts explain differences in targeting strategies, highlighting the need for more research beyond the global north. Finally, our results have important implications for policy makers, and other stakeholders who seek to develop regulations to address the challenges posed by political microtargeting techniques.","PeriodicalId":93587,"journal":{"name":"Journal of quantitative description: digital media","volume":"42 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141029985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The incel (involuntary celibate) community is an extremist online community that practices intense misogyny, racism, and that glorifies – and sometimes practices - violence. Work to understand the dynamics within incel communities has been hindered by the fact that these communities are spread over many platforms and many of the more popular forums of the past have been banned and their content deleted. In this paper, we present two main contributions. First, we introduce a carefully reconstructed, nearly complete archive of incel forums dating back to 2016, including millions of posts that can no longer be accessed. Then we illustrate a technique for identifying community-specific language and using that as a marker of extremism to track radicalization over time.
{"title":"A Dataset for The Study of Online Radicalization Through Incel Forum Archives","authors":"Jennifer Golbeck","doi":"10.51685/jqd.2024.004","DOIUrl":"https://doi.org/10.51685/jqd.2024.004","url":null,"abstract":"The incel (involuntary celibate) community is an extremist online community that practices intense misogyny, racism, and that glorifies – and sometimes practices - violence. Work to understand the dynamics within incel communities has been hindered by the fact that these communities are spread over many platforms and many of the more popular forums of the past have been banned and their content deleted. In this paper, we present two main contributions. First, we introduce a carefully reconstructed, nearly complete archive of incel forums dating back to 2016, including millions of posts that can no longer be accessed. Then we illustrate a technique for identifying community-specific language and using that as a marker of extremism to track radicalization over time. ","PeriodicalId":93587,"journal":{"name":"Journal of quantitative description: digital media","volume":"2 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140712168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Valerie Wirtschafter, Frederico Batista Pereira, Natália Bueno, N. Pavão, Jo˜ao Pedro Oliveira dos Santos, Felipe Nunes
We provide and examine an approach for detecting false stories that circulate as text and without hyperlinks, which are commonly found in the Global South. Our text-based approach relies on a combination of false stories identified by fact-checkers, supervised learning methods, natural language processing, and human review. We contrast our approach with the established domain-based and with Facebook’s URL approaches by applying them in the case of Brazilian political leaders. The results show that sharing false news by politicians is a rare event: less than 1% of political leaders’ social media posts contain misinformation. However, we find little overlap across the approaches. The text-based approach leads to different conclusions about which politicians share misinformation and the type of false content shared, while demographic and political predictors of misinformation-sharing behavior are typically similar across approaches. Our approach produces fewer false positives than other approaches and only a small number of false negatives. Our results show that the text-based approach is an important complement to the dominant approaches as it is more effective at detecting false news.
{"title":"Detecting Misinformation: Identifying False News Spread by Political Leaders in the Global South","authors":"Valerie Wirtschafter, Frederico Batista Pereira, Natália Bueno, N. Pavão, Jo˜ao Pedro Oliveira dos Santos, Felipe Nunes","doi":"10.51685/jqd.2024.007","DOIUrl":"https://doi.org/10.51685/jqd.2024.007","url":null,"abstract":"\u0000\u0000\u0000We provide and examine an approach for detecting false stories that circulate as text and without hyperlinks, which are commonly found in the Global South. Our text-based approach relies on a combination of false stories identified by fact-checkers, supervised learning methods, natural language processing, and human review. We contrast our approach with the established domain-based and with Facebook’s URL approaches by applying them in the case of Brazilian political leaders. The results show that sharing false news by politicians is a rare event: less than 1% of political leaders’ social media posts contain misinformation. However, we find little overlap across the approaches. The text-based approach leads to different conclusions about which politicians share misinformation and the type of false content shared, while demographic and political predictors of misinformation-sharing behavior are typically similar across approaches. Our approach produces fewer false positives than other approaches and only a small number of false negatives. Our results show that the text-based approach is an important complement to the dominant approaches as it is more effective at detecting false news.\u0000\u0000\u0000","PeriodicalId":93587,"journal":{"name":"Journal of quantitative description: digital media","volume":"372 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140472152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Expanding global usage of social media and growing questions about its societal impact have led scholars to investigate the relationship between individuals' offline and online behaviors and characteristics. Such inquiries, which compare individuals' survey responses to their social media behavior, typically do not address whether the elicitation of survey respondents' social media information introduces any systematic errors. However, making inferences from a survey-linked sample to a social media platform, and finally to a survey sample or broader target population, can be imperiled when systematic differences exist between those who provide and those who deny researchers access to their social media accounts. In this paper, we ask: Do survey respondents who say they use Twitter differ from the subset providing validated Twitter handles, as well as from the overall survey sample? Pooling across five datasets and over 31,000 respondents, we show first that samples of stated Twitter users differ from the initial survey samples from which they are drawn on several socio-demographic characteristics. Second and reassuringly as concerns possible errors due to survey-linkage, we report few systematic differences between those who say they use Twitter and those who provide validated Twitter handles. Nevertheless, we do document differences on some demographics, and we illustrate how errors could carry potential consequences for sample composition of which researchers should be aware. Finally, we conclude with a discussion of our results, their possible generalizability, and areas for future research.
{"title":"@Who? Investigating Possible Errors in Studies Linking Survey and Twitter Data","authors":"Marten Appel, Nicholas Haas","doi":"10.51685/jqd.2024.002","DOIUrl":"https://doi.org/10.51685/jqd.2024.002","url":null,"abstract":"\u0000\u0000\u0000Expanding global usage of social media and growing questions about its societal impact have led scholars to investigate the relationship between individuals' offline and online behaviors and characteristics. Such inquiries, which compare individuals' survey responses to their social media behavior, typically do not address whether the elicitation of survey respondents' social media information introduces any systematic errors. However, making inferences from a survey-linked sample to a social media platform, and finally to a survey sample or broader target population, can be imperiled when systematic differences exist between those who provide and those who deny researchers access to their social media accounts. In this paper, we ask: Do survey respondents who say they use Twitter differ from the subset providing validated Twitter handles, as well as from the overall survey sample? Pooling across five datasets and over 31,000 respondents, we show first that samples of stated Twitter users differ from the initial survey samples from which they are drawn on several socio-demographic characteristics. Second and reassuringly as concerns possible errors due to survey-linkage, we report few systematic differences between those who say they use Twitter and those who provide validated Twitter handles. Nevertheless, we do document differences on some demographics, and we illustrate how errors could carry potential consequences for sample composition of which researchers should be aware. Finally, we conclude with a discussion of our results, their possible generalizability, and areas for future research.\u0000\u0000\u0000","PeriodicalId":93587,"journal":{"name":"Journal of quantitative description: digital media","volume":"91 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139612891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The practice of political micro-targeting (PMT) – tailoring messages for voters based on their personal data – has increased over the past two decades, particularly in the U.S. Studies of PMT have to date concentrated largely on its effects on voters, or its implications for democracy more broadly. Less attention has been given to answering basic descriptive questions about how people perceive, feel and care about this new mode of political communication. This paper fills that gap by reporting findings from an online survey (weighted to be nationally representative on age, gender, ethnicity, region and past vote) that measured public attitudes toward PMT during the 2020 U.S. Presidential campaign. Specifically, we measure voter orientations toward PMT in four key dimensions – awareness, aversion, knowledge, and acceptability at the aggregate level – and explore how these vary according to a range of individual characteristics. Key findings are that public understanding and acceptance of PMT may be higher than current studies indicate, particularly among certain sectors of the population. Such insights are important for academic research to cognize and also policy-makers, as they move toward greater regulation of voter targeting.
{"title":"\"I always feel like somebody's watching me\"","authors":"Rachel Gibson, Esmeralda Bon, Katherine Dommett","doi":"10.51685/jqd.2024.001","DOIUrl":"https://doi.org/10.51685/jqd.2024.001","url":null,"abstract":"The practice of political micro-targeting (PMT) – tailoring messages for voters based on their personal data – has increased over the past two decades, particularly in the U.S. Studies of PMT have to date concentrated largely on its effects on voters, or its implications for democracy more broadly. Less attention has been given to answering basic descriptive questions about how people perceive, feel and care about this new mode of political communication. This paper fills that gap by reporting findings from an online survey (weighted to be nationally representative on age, gender, ethnicity, region and past vote) that measured public attitudes toward PMT during the 2020 U.S. Presidential campaign. Specifically, we measure voter orientations toward PMT in four key dimensions – awareness, aversion, knowledge, and acceptability at the aggregate level – and explore how these vary according to a range of individual characteristics. Key findings are that public understanding and acceptance of PMT may be higher than current studies indicate, particularly among certain sectors of the population. Such insights are important for academic research to cognize and also policy-makers, as they move toward greater regulation of voter targeting.","PeriodicalId":93587,"journal":{"name":"Journal of quantitative description: digital media","volume":"35 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139128940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ryan McGrady, Kevin Zheng, Rebecca Curran, Jason Baumgartner, Ethan Zuckerman
YouTube is one of the largest, most important communication platforms in the world, but while there is a great deal of research about the site, many of its fundamental characteristics remain unknown. To better understand YouTube as a whole, we created a random sample of videos using a new method. Through a description of the sample’s metadata, we provide answers to many essential questions about, for example, the distribution of views, comments, likes, subscribers, and categories. Our method also allows us to estimate the total number of publicly visible videos on YouTube and its growth over time. To learn more about video content, we hand-coded a subsample to answer questions like how many are primarily music, video games, or still images. Finally, we processed the videos’ audio using language detection software to determine the distribution of spoken languages. In providing basic information about YouTube as a whole, we not only learn more about an influential platform, but also provide baseline context against which samples in more focused studies can be compared.
{"title":"Dialing for Videos: A Random Sample of YouTube","authors":"Ryan McGrady, Kevin Zheng, Rebecca Curran, Jason Baumgartner, Ethan Zuckerman","doi":"10.51685/jqd.2023.022","DOIUrl":"https://doi.org/10.51685/jqd.2023.022","url":null,"abstract":"YouTube is one of the largest, most important communication platforms in the world, but while there is a great deal of research about the site, many of its fundamental characteristics remain unknown. To better understand YouTube as a whole, we created a random sample of videos using a new method. Through a description of the sample’s metadata, we provide answers to many essential questions about, for example, the distribution of views, comments, likes, subscribers, and categories. Our method also allows us to estimate the total number of publicly visible videos on YouTube and its growth over time. To learn more about video content, we hand-coded a subsample to answer questions like how many are primarily music, video games, or still images. Finally, we processed the videos’ audio using language detection software to determine the distribution of spoken languages. In providing basic information about YouTube as a whole, we not only learn more about an influential platform, but also provide baseline context against which samples in more focused studies can be compared.","PeriodicalId":93587,"journal":{"name":"Journal of quantitative description: digital media","volume":"65 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138956962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Members of Congress increasingly rely on social media to communicate with their constituents and other members of the public in real time. However, despite their increased use, little is known about the composition of members' audiences in these online spaces. We address these questions using a panel of Twitter users linked to their congressional district of residence through administrative data. We provide evidence that Twitter users who followed their own representative in the 115th, 116th, and 117th Congresses were generally older and more partisan, and live in wealthier areas of those districts, compared to those who did not. We further find that shared partisanship and shared membership in historically marginalized groups are associated with an increased probability of a constituent following their congressional representative. These results suggest that the efficiency of communication offered by social media reproduces, rather than alters, patterns of political polarization and class inequalities in representation observed offline.
{"title":"Inequalities in Online Representation: Who Follows Their Own Member of Congress on Twitter?","authors":"Stefan McCabe, Jon Green, Pranav Goel, D. Lazer","doi":"10.51685/jqd.2023.021","DOIUrl":"https://doi.org/10.51685/jqd.2023.021","url":null,"abstract":"Members of Congress increasingly rely on social media to communicate with their constituents and other members of the public in real time. However, despite their increased use, little is known about the composition of members' audiences in these online spaces. We address these questions using a panel of Twitter users linked to their congressional district of residence through administrative data. We provide evidence that Twitter users who followed their own representative in the 115th, 116th, and 117th Congresses were generally older and more partisan, and live in wealthier areas of those districts, compared to those who did not. We further find that shared partisanship and shared membership in historically marginalized groups are associated with an increased probability of a constituent following their congressional representative. These results suggest that the efficiency of communication offered by social media reproduces, rather than alters, patterns of political polarization and class inequalities in representation observed offline.","PeriodicalId":93587,"journal":{"name":"Journal of quantitative description: digital media","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139000354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper contributes to the ongoing effort to describe and quantify the quality of information that is shared on large social media platforms. We do this by complementing existing research that provided a first quantitative assessment of the quality of the information circulating on Facebook among US users. Leveraging an updated version of the same data source — Meta's URL Shares Dataset — and replicating much of the methodology, we quantify the trustworthy and untrustworthy links to external websites that have been shared on Facebook in the period between 2019 and 2022 in three major European countries (Germany, France, and Italy). We observe a clear decline in the number of URLs present in the dataset and an increase in the URLs from untrustworthy domains as a percentage of the total URLs shared in a year. This increase seems to be higher in electoral years (in Germany and in Italy) but it does not translate into an increase of Views received from untrustworthy sources.
{"title":"Cracking Open the European Newsfeed","authors":"Luca Rossi, Fabio Giglietto, Giada Marino","doi":"10.51685/jqd.2023.020","DOIUrl":"https://doi.org/10.51685/jqd.2023.020","url":null,"abstract":"This paper contributes to the ongoing effort to describe and quantify the quality of information that is shared on large social media platforms. We do this by complementing existing research that provided a first quantitative assessment of the quality of the information circulating on Facebook among US users. Leveraging an updated version of the same data source — Meta's URL Shares Dataset — and replicating much of the methodology, we quantify the trustworthy and untrustworthy links to external websites that have been shared on Facebook in the period between 2019 and 2022 in three major European countries (Germany, France, and Italy). We observe a clear decline in the number of URLs present in the dataset and an increase in the URLs from untrustworthy domains as a percentage of the total URLs shared in a year. This increase seems to be higher in electoral years (in Germany and in Italy) but it does not translate into an increase of Views received from untrustworthy sources.","PeriodicalId":93587,"journal":{"name":"Journal of quantitative description: digital media","volume":"33 S1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139009932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Research on ideological biases and polarization on social media platforms primarilyfocuses on news and political content. Non-political content, which isvastly more popular, is often overlooked. Because partisanship is correlatedwith citizens’ non-political attitudes and non-political content can carry politicalcues, we explore whether ideological biases and partisan segregation extendto users’ non-political exposures online. We focus on YouTube, one of the mostpopular platforms. We rely online data from American adults (N = 2,237).From over 129 million visits to over 37 million URLs, we analyze 1,037,392visits to YouTube videos from 1,874 participants. We identify YouTube channelsof 942 news domains, utilize a BERT-based classifier to identify politicalvideos outside news channels, and estimate the ideology of all the videos inour data. We compare ideological biases in exposure to (a) news, (b) political,and (c) non-political content. We examine both exposure congeniality (i.e., areusers consuming like-minded content?) and polarization (i.e. are there overlapsbetween Democrats and Republicans in the content they consume?). Wefind substantial congeniality in the consumption of news and political videos,especially among Republicans, and high levels of polarization in this exposure(i.e., limited overlaps between Democrats and Republicans). We also showthat both exposure congeniality and polarization are significantly lower fornon-political content, in that non-political videos are less likely to be ideologicallylike-minded and both Democrats and Republicans consume similarnon-political content. Theoretical and practical implications of these findingsare discussed.
{"title":"Political content and news are polarized but other content is not in YouTube watch histories","authors":"Magdalena Wojcieszak, Rong-Ching (Anna) Chang, Ericka Menchen-Trevino","doi":"10.51685/jqd.2023.018","DOIUrl":"https://doi.org/10.51685/jqd.2023.018","url":null,"abstract":"Research on ideological biases and polarization on social media platforms primarilyfocuses on news and political content. Non-political content, which isvastly more popular, is often overlooked. Because partisanship is correlatedwith citizens’ non-political attitudes and non-political content can carry politicalcues, we explore whether ideological biases and partisan segregation extendto users’ non-political exposures online. We focus on YouTube, one of the mostpopular platforms. We rely online data from American adults (N = 2,237).From over 129 million visits to over 37 million URLs, we analyze 1,037,392visits to YouTube videos from 1,874 participants. We identify YouTube channelsof 942 news domains, utilize a BERT-based classifier to identify politicalvideos outside news channels, and estimate the ideology of all the videos inour data. We compare ideological biases in exposure to (a) news, (b) political,and (c) non-political content. We examine both exposure congeniality (i.e., areusers consuming like-minded content?) and polarization (i.e. are there overlapsbetween Democrats and Republicans in the content they consume?). Wefind substantial congeniality in the consumption of news and political videos,especially among Republicans, and high levels of polarization in this exposure(i.e., limited overlaps between Democrats and Republicans). We also showthat both exposure congeniality and polarization are significantly lower fornon-political content, in that non-political videos are less likely to be ideologicallylike-minded and both Democrats and Republicans consume similarnon-political content. Theoretical and practical implications of these findingsare discussed.","PeriodicalId":93587,"journal":{"name":"Journal of quantitative description: digital media","volume":"81 24","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135091919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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