Digital Trace Data Collection for Social Media Effects Research: APIs, Data Donation, and (Screen) Tracking

Ohme, Jakob; Araujo, Theo; Boeschoten, Laura; Freelon, Deen; Ram, Nilam; Reeves, Byron; Robinson, Thomas N.

doi:10.1080/19312458.2023.2181319

Cited by 50 publications

(20 citation statements)

References 73 publications

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“…For the researcher, the advantage of data donation lies in not having to rely on a commercial platform providing an API for structured access to data. Instead, they can go directly to the users to access rich individual data, including information that typically cannot be accessed via trackers (e.g., behavior within an app), and that can be further linked to information participants provide in a survey (Ohme et al 2023). Compared to trackers that need to be installed on all devices to receive a complete picture of a user's behavior on that platform (Bosch and Revilla 2022), DDPs include information on all activities regardless of the device used (Breuer et al 2022).…”

Section: Data Donation As a Novel Form Of Digital Trace Data Collectionmentioning

confidence: 99%

“…Compared to trackers that need to be installed on all devices to receive a complete picture of a user's behavior on that platform (Bosch and Revilla 2022), DDPs include information on all activities regardless of the device used (Breuer et al 2022). For the participant, data donation provides more control over what data are shared with the researcher (Ohme et al 2023). Other than in the case of a tracker, which, once installed, continuously collects data in the background of a user's device, participants actively donate their existing, retrospective data.…”

Section: Data Donation As a Novel Form Of Digital Trace Data Collectionmentioning

confidence: 99%

“…Different methods are available for collecting individual-level digital trace data, with most of them falling into one of three categories (Breuer et al 2020;Ohme et al 2023). First, application programming interfaces (APIs) afford researchers direct access to data from social media platforms.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Do you have two minutes to talk about your data? Willingness to participate and nonparticipation bias in Facebook data donation

Keusch,

Pankowska,

Cernat

et al. 2023

Preprint

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Data donation is a novel approach to collecting digital trace data, where users are asked to download their retrospective data from a platform and share them with the researchers. Little is known about the willingness to donate data and the potential bias that may arise from nonparticipation. We conducted a study among over 900 German Facebook users asking them to donate two data packages. While around 80 percent of participants were willing to donate their data, only around one third of them successfully did so. Trust in researchers positively correlates with willingness and donation success, and trust in Facebook is negatively associated with donation success. The framing of the data donation request did not affect the outcomes. We find no difference in frequency of Facebook use between donors and non-donors.

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Section: Data Donation As a Novel Form Of Digital Trace Data Collectionmentioning

confidence: 99%

Section: Data Donation As a Novel Form Of Digital Trace Data Collectionmentioning

confidence: 99%

See 1 more Smart Citation

Do you have two minutes to talk about your data? Willingness to participate and nonparticipation bias in Facebook data donation

Keusch,

Pankowska,

Cernat

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…These include crawling OSN platforms using Application Programming Interfaces (APIs), web scraping, network dataset repositories (pre-collected), data sharing agreements, and digital trace data collection, among others (Ohme et al, 2023;Stark, 2018). The most popular technique to collect real-time data analysis is through the use of API, as it provides researchers with the capability to retrieve and gather data in real-time, facilitating immediate analysis of the data upon its entry into the system (Ohme et al, 2023;Stark, 2018;Venturini & Rogers, 2019;Weber et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Generating Attribute Similarity Graphs: A User Behavior-Based Approach from Real- Time Microblogging Data on Platform X

Hasan,

Bakar,

Yaakub

2024

Preprint

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Social network analysis is a powerful tool for understanding various phenomena, but it requires data with explicit connections among users. However, such data is hard to obtain in real-time, especially from platforms like X, commonly known as Twitter, where users share topic-related content rather than personal connections. Therefore, this paper tackles a new problem of building a social network graph in real-time where explicit connections are unavailable. Our methodology is centred around the concept of user similarity as the fundamental basis for establishing connections, suggesting that users with similar characteristics are more likely to form connections. To implement this concept, we extracted easily accessible attributes from the Twitter platform and proposed a novel graph model based on similarity. We also introduce an Attribute-Weighted Euclidean Distance (AWED) to calculate user similarities. We compare the proposed graph with synthetic graphs based on network properties, online social network characteristics, and predictive analysis. The results suggest that the AWED graph provides a more precise representation of the dynamic connections that exist in real-world online social networks, surpassing the inherent constraints of synthetic graphs. We demonstrate that the proposed method of graph construction is simple, flexible, and effective for network analysis tasks.

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“…EWOM and social media content are crucial components of social media marketing to increase engagement [22,23]. Research in this field is still limited and challenging to comprehend despite researchers' increased attention to content marketing and engagement [24,25]. This study aims to fill the empirical gap by re-examining the impact of content types on engagement and engagement on EWOM and testing for the first time the role of engagement in mediating the relationship between content and EWOM on the social media Facebook page of the e-commerce industry.…”

Section: Introductionmentioning

confidence: 99%

Investigating the role of rational and emotional content towards consumer engagement and EWOM intention: Uses and gratification perspectives

Martini,

Hurriyati,

Sultan

2023

ijirss

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This study aims to examine the role of rational and emotional content in social media on consumer engagement and EWOM intention. The population of this research is e-commerce social media users with a total sample size of 394 respondents using the purposive sampling technique. The Partial Least Square-Structural Equation Model (PLS-SEM) is used to test the research hypothesis. The results of the study revealed the role of rational and emotional content in influencing consumer engagement but did not affect EWOM intention. Consumer engagement affects EWOM intentions. The study’s findings support consumer engagement which is proven to mediate the relationship between rational and emotional content and EWOM intention. Social media marketing practitioners use rational and emotional content types on their brand pages in order to gain engagement and EWOM from social media users.

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Digital Trace Data Collection for Social Media Effects Research: APIs, Data Donation, and (Screen) Tracking

Cited by 50 publications

References 73 publications

Do you have two minutes to talk about your data? Willingness to participate and nonparticipation bias in Facebook data donation

Do you have two minutes to talk about your data? Willingness to participate and nonparticipation bias in Facebook data donation

Generating Attribute Similarity Graphs: A User Behavior-Based Approach from Real- Time Microblogging Data on Platform X

Investigating the role of rational and emotional content towards consumer engagement and EWOM intention: Uses and gratification perspectives

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