Relevance & Research Question:

Ad spending in social media is projected to reach US$110,628m in 2021. In this context, the smartphone is by far the tool with which people spend the most time on social media. In Germany, the top social media smartphone apps for 2020 were Instagram (23min), YouTube (22min) and Facebook (14min). However, getting access to real and independent performance data for ads shown to specific target groups is technically, and from a data privacy point of view, a huge challenge

Methods & Data:

We have built a new method to access in-app social media advertising data and interaction data on smartphones. By voluntarily installing an app for study purposes, participants passively provide information for all in-app advertisements they see and interact with on Facebook, YouTube, Instagram. To detect and process the data we use machine learning methods and smartphone-sensing technology. Data is only used for study purposes, in compliance with GDPR and German Market and Social Research standards. In a first test study with respondi, we have looked at 50 Facebook-app users who participated for 45 days on average in Feb-May 2021. We saw over 91.000 Facebook ads in total from more than 8.000 publishers – top ad publishers were Amazon and Wish.

Results:

Our methods provide granular data about deployment and success of social media ads from all industries and competitors. They also reveal which target groups are exposed to which ads, e.g. by company and product category. With natural language processing and machine learning algorithms it is possible to improve ad-targeting and ad-content based on real-world ad-performance data: What are most successful ads (i.e. language, text length, emojis), which target group(s) are they served to, and in which frequency. Interaction data from participants (e.g. ad clicks) reveals the viral potential of individual ad campaigns.

Added Value:

Our method offers an easy to use, GDPR-compliant way to analyze real social media ads on smartphones. The app is easy to download and install from the Google Playstore. After installation, it runs in the background without any need for further user-interaction, which minimizes attention bias.

Keywords: Surveys, Social media, Twitter, Data linkage, Consent, Ethics, Cognitive testing

Relevance & Research Question:

Linking survey and social media data can enhance both. For example, survey data can benefit from additional data covering areas not included in the original questionnaire, while social media data can benefit from survey data’s structure and direction.

A key methodological challenge is collecting informed consent. Striking a balance between providing enough information that consent is ‘informed’ while not overwhelming participants is difficult. In addition, previous research has found consent rates to be low, particularly in web surveys, potentially reducing the usefulness of a linked dataset.

Consulting the public can help to ensure protocols developed for asking consent are ethical and effective. This study looks at how can we encourage informed consent to link social media, specifically Twitter, and survey data.

Methods & Data:

This study develops methods previously used for understanding consent to link survey data and administrative records. A total of 25 interviews will be conducted with a purposive sample of British adults using a mixture of cognitive and depth interviewing techniques. Participants will initially be asked to complete a short questionnaire, including a question asking for their consent to link their survey and Twitter data, during which they will be encouraged to ‘think aloud’. Following this, cognitive probes will be used to explore the participants’ decision making process and understanding of the consent question, before opening up into a wider discussion of their attitudes to data linkage of survey and social media data.

Results:

Fieldwork is underway at the time of submission. We expect results to provide insight into people’s understanding of the consent question (and therefore the extent to which any consent decision is informed), and what may be encouraging or discouraging people from consenting.

Added Value:

Findings from this study will help to inform the future design of consent questions, with the goal of improving informed consent rates and therefore data quality. It will also provide evidence of the public acceptability of this approach and how protocols developed for collecting, analysing, archiving and sharing data can best address any concerns.

Relevance & Research Question: Computational social science research on socioeconomic inequality has been constrained by the lack of individual-level socioeconomic status (SES) measures in digital trace data. Even for the most researched social media platform, Twitter, there is an inadequate number of existing studies on estimating the SES of individual users, and most of them have methodological limitations. To fill the gap, we propose an unsupervised learning method that is firmly embedded in sociological theory.

Methods & Data: Following Bourdieu, we argue that the commercial and entertainment brands that Twitter users follow reflect their economic and cultural capital and hence, these followings can be used to infer the users’ SES. Our method parallels an established political science approach to estimate Twitter users’ political ideology from the political actors they follow. We start with the official Twitter accounts of popular brands and employ correspondence analysis to project the brands and their followers onto a linear SES scale. Using this method, we estimate the SES of 3,484,521 Twitter users who follow the Twitter accounts of 342 brands in the United States.

Results: The results show reasonable correlations between our SES estimates and the standard proxies for SES. We validate the measure for 50 common job titles, identifying 61,091 users who state one of the titles in their profile description and find significant correlations between median estimated SES and income (ρ = 0.668, p < 0.001) and median estimated SES and occupational class (ρ = 0.653, p < 0.001). We further use audience estimation data from the Facebook Marketing API to verify that the brands’ estimated SES is significantly associated with their audience’s educational level.

Added Value: Compared to the existing approaches, our method requires less data, fewer steps, and simpler statistical procedures while, at the same time, returns estimates for a larger set of users. The method provides SES estimates on a continuous scale that are operationally easy to use and theoretically interpretable. Social scientists could combine these SES estimates with digital trace data on behaviours, communication patterns, and social interactions to study inequality, health, and political engagement, among other topics.