Deploying links to professional fact-checking websites (so-called “snoping”) is a common misinformation intervention technique that can be used by social media users to refute misleading claims made by others. However, the real-world effect of snoping may be limited as it suffers from low visibility and distrust towards professional fact-checkers. As a remedy, X (formerly known as Twitter) recently launched its community-based fact-checking system “Community Notes” on which fact-checks are carried out by actual X users and directly shown on the fact-checked posts. Yet, an understanding of how fact-checking via Community Notes differs from regular snoping is largely absent. In this study, we empirically analyze differences in how contributors to Community Notes and Snopers select their targets when fact-checking social media posts. For this purpose, we collect and holistically analyze two unique datasets from X: (a) 25,912 community-created fact-checks from X's Community Notes platform, and (b) 52,505 “snopes” that debunk posts via fact-checking replies that link to professional fact-checking websites. We find that Notes contributors and Snopers focus on different targets when fact-checking social media content. For instance, Notes contributors tend to fact-check posts from larger accounts with higher social influence and are relatively less likely to emphasize the accuracy of non-misleading posts. Fact-checking targets of Notes contributors and Snopers rarely overlap; however, those overlapping exhibit a high level of agreement in the fact-checking assessment. Moreover, we demonstrate that Snopers fact-check social media posts at a higher speed. Altogether, our findings imply that different fact-checking approaches – carried out on the same social media platform – can result in vastly different social media posts getting fact-checked. This has important implications for future research on misinformation, which should not rely on a single fact-checking approach when compiling misinformation datasets. From a practical perspective, our findings imply that different fact-checking approaches complement each other and may help social media providers to optimize strategies to combat misinformation on their platforms.

Social media has become an indispensable channel for political communication. However, the political discourse is increasingly characterized by hate speech, which affects not only the reputation of individual politicians but also the functioning of society at large. In this work, we empirically analyze how the amount of hate speech in replies to posts from politicians on Twitter depends on personal characteristics, such as their party affiliation, gender, and ethnicity. For this purpose, we employ Twitter's Historical API to collect every tweet posted by members of the 117th U.S. Congress for an observation period of more than six months. Additionally, we gather replies for each tweet and use machine learning to predict the amount of hate speech they embed. Subsequently, we implement hierarchical regression models to analyze whether politicians with certain characteristics receive more hate speech. We find that tweets are particularly likely to receive hate speech in replies if they are authored by (i) persons of color from the Democratic party, (ii) white Republicans, and (iii) women. Furthermore, our analysis reveals that more negative sentiment (in the source tweet) is associated with more hate speech (in replies). However, the association varies across parties: negative sentiment attracts more hate speech for Democrats (vs. Republicans). Altogether, our empirical findings imply significant differences in how politicians are treated on social media depending on their party affiliation, gender, and ethnicity.

While false rumors pose a threat to the successful overcoming of the COVID-19 pandemic, an understanding of how rumors diffuse in online social networks is – even for non-crisis situations – still in its infancy. Here we analyze a large sample consisting of COVID-19 rumor cascades from Twitter that have been fact-checked by third-party organizations. The data comprises N = 10,610 rumor cascades that have been retweeted more than 24 million times. We investigate whether COVID-19 misinformation spreads more viral than the truth and whether the differences in the diffusion of true vs. false rumors can be explained by the moral emotions they carry. We observe that, on average, COVID-19 misinformation is more likely to go viral than truthful information. However, the veracity effect is moderated by moral emotions: false rumors are more viral than the truth if the source tweets embed a high number of other-condemning emotion words, whereas a higher number of self-conscious emotion words is linked to a less viral spread. The effects are pronounced both for health misinformation and false political rumors. These findings offer insights into how true vs. false rumors spread and highlight the importance of considering emotions from the moral emotion families in social media content.

Online real estate platforms have become significant marketplaces facilitating users’ search for an apartment or a house. Yet it remains challenging to accurately appraise a property’s value. Prior works have primarily studied real estate valuation based on hedonic price models that take structured data into account while accompanying unstructured data is typically ignored. In this study, we investigate to what extent an automated visual analysis of apartment floor plans on online real estate platforms can enhance hedonic rent price appraisal. We propose a tailored two-staged deep learning approach to learn price-relevant designs of floor plans from historical price data. Subsequently, we integrate the floor plan predictions into hedonic rent price models that account for both structural and locational characteristics of an apartment. Our empirical analysis based on a unique dataset of 9,174 real estate listings suggests that current hedonic models underutilize the available data. We find that (1) the visual design of floor plans has significant explanatory power regarding rent prices – even after controlling for structural and locational apartment characteristics, and (2) harnessing floor plans results in an up to 10.56 % lower out-of-sample prediction error. We further find that floor plans yield a particularly high gain in prediction performance for older and smaller apartments. Altogether, our empirical findings contribute to the existing research body by establishing the link between the visual design of floor plans and real estate prices. Moreover, our approach has important implications for online real estate platforms, which can use our findings to enhance user experience in their real estate listings.