Relevance & Research Question

For years, researchers have attempted to use social media data to generate inferences typically produced using surveys. But Twitter data and other social media traces do not consistently reflect contemporary survey findings. Two explanations have been proposed for why this might be the case: one posits that the set of people producing data on social media sites differs from those recruited to surveys; the other asserts that data generating processes are sufficiently different that it does not make sense to compare their social media and survey outputs directly.

Methods & Data

This study links a probability US sample of survey respondents with those same individuals’ Twitter data as well as with decahose Twitter data. We compare four datasets to understand links between samples and data generating processes. These include survey responses on three topics for (1) a probability sample of the US public (N=9544); (2) the same survey responses for the subset of individuals who use Twitter, consent to access, and tweet about the topics of interest (N=246); (3) tweets for this set of linked individuals who tweeted about the topic of interest; and (4) tweets from US individuals sampled from the Twitter decahose (N=7,363 after removing bots and non-individual accounts). Open-ended survey questions and social media posts are topic modeled using a guided topic modeling approach within topic areas to identify vaccination behaviors/attitudes, economic evaluations, and parenting challenges during the COVID pandemic.
Results

We find that the subset of individuals who used Twitter and consented to linkage differed slightly in demographic composition, but mentioned similar distribution subtopics in response to open-ended survey questions about all three areas. In contrast, individuals with survey and Twitter data provided similar data across these two modes for one of our three topics (economics) and different data across the other topics (vaccinations and parenting). Tweets from consented users and the decahose sample, in contrast, provided similar distributions of topics for vaccinations and parenting, but not economics.
Added Value

This suggests that motivation to post and posting frequency may be more important for data acquired than who is represented.

Relevance & Research Question

In the context of an online social network where geographical distance is often assumed to be inconsequential, this study examines how physical proximity relates to Twitter user interaction. In line with previous findings, the central hypothesis is that individuals who live in closer physical proximity are more likely to engage with one another, despite the virtual nature of Twitter. Moreover, the extent of this impact is expected to be contingent on the specific topic under discussion.

Methods & Data

Employing a multi-layered approach, the study integrates techniques from natural language processing, network analysis, and spatial analysis. A dataset of over 500 million geolocated German tweets (including retweets) forms the basis of the analysis. First, a BERT-like language model is trained on the tweets to categorise them into thematically similar groups, enabling a granular exploration of topic-specific interactions. Subsequently, retweet and reply networks are constructed for each thematic group as well as for the entire tweet corpus. Community detection algorithms are then used to identify clusters of users who frequently retweet and reply to each other. Spatial analysis is then applied to examine the correlation between users' physical proximity and their clustering as identified by community detection.

Results

Preliminary results indicate a corpus-wide positive correlation between the spatial proximity of users and their clustering based on retweet and reply communities. However, the strength and significance of the correlation varies across the different topics discussed within the Twitter dataset. Notably, the geographical aspect of discussions can be found not only among local topics, but also in topics with a more universal appeal.

Added Value

This study offers a methodologically complex investigation of the interplay between geography and online social networks. By revealing the nuanced relationship between spatial proximity and Twitter user interaction based on topics, the study extends our understanding of online social dynamics. The findings contribute to the broader discourse on social media by highlighting the importance of local context and regional differences as a determinant of online interaction patterns.

Relevance & Research Question

Women and men are portrayed differently in advertising and on social media, as research on gender (self-)portrayal has shown. Most studies in this area analyzed small samples of static images to examine gender stereotypes conveyed through images on social media. We study gender (self-)portrayal on TikTok, in particular which dynamic expressions are more often used by individuals passing as women or men. For this, we present a novel method to analyze large amounts of video data with computational methods.

Methods & Data

Our data encompasses approximately 36,000 unique videos extracted from the top 1000 trending TikTok videos in Germany over a consecutive 40-day period in 2021, supplemented by 973,000 metadata entries. Each video is processed using YOLOv8 pose detection, which dissects the videos into frames and annotates 17 key points per frame. We group the data into commonly used dynamic expressions (i.e., sequences of body movement). We employ HDBSCAN and DTW to deal with differences of sequence and video length and to handle ‘valid’ missing data, e.g., from certain body parts not being visible in the footage.

Results

Sequences are grouped into prototypes of dynamic expressions. Using manually annotated information, we can distinguish certain types of movement that are more commonly used by one gender. Utilizing metadata and expressions in the videos, we are able to explain a part of the variance of how a video performs, i.e. how many likes it gets or how long it stays within the top 1000 trends. A qualitative assessment of the prototypes of the most gender-biased expressions allows for integration with sociological theory on gender stereotypical body posing and provides insight into why some poses might perform better regarding likes and views.

Added Value

We extend the framework for analyzing gender stereotypical posing from static social media images to dynamic social media videos, which is an important endeavor to adapt to the trend of video-based social media content (Snapchat, TikTok, Instagram reels) becoming the de facto default type of content, especially for younger generations. Regarding methods, we offer a tractable way to analyze body posing on social media.