Clinical trials are crucial for the advancement of treatment and knowledge within the medical community. Since 2007, US federal government took the initiative and requires organizations sponsoring clinical trials with at least one site in the United States to submit information on these clinical trials to the ClinicalTrials.gov database, resulting in a rich source of information for clinical trial research. Nevertheless, only a handful of analytic studies have been carried out to understand this valuable data source. In this study, we propose to use network analysis to understand infectious disease clinical trial research. Our goal is to answer two important questions: (1) what are the concentrations and characteristics of infectious disease clinical trail research? and (2) how to accurately predict what type of clinical trials a sponsor (or an investigator) is interested in? The answers to the first question provide effective ways to summarize clinical trial research related to particular disease(s), and the answers to the second question help match clinical trial sponsors and investigators for information recommendation. By using 4,228 clinical trails as the test bed, our study involves 4,864 sponsors and 1,879 research areas characterized by Medical Subject Heading (MeSH) keywords. We extract a set of network measures to show patterns of infectious disease clinical trials, and design a new community based link prediction approach to predict sponsors' interests, with significant improvement compared to baselines. This trans-formative study concludes that using network analysis can tremendously help the understanding of clinical trial research for effective summarization, characterization, and prediction.
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Quantifying Data-Driven Campaigning Across Sponsors and Platforms
Although modern data-driven campaigning (DDC) is not entirely new, scholars have typically relied on reports and interviews of practitioners to understand its use. However, the advent of public ad libraries from Meta and Google provides an opportunity to measure the scope and variation in DDC practice in advertising across different types of sponsors and within sponsors across platforms. Using textual and audiovisual processing, we create a database of ads from the 2022 US elections. These data allow us to create an index that quantifies the extent of DDC at the level of the sponsor and platform. This index takes into account both the number of unique creatives placed and the similarity across those creatives. In addition, we explore the impact of sponsor resources, the office being sought, and the competitiveness of the race on the measure of DDC sophistication. Ultimately, our research establishes a measurement strategy for DDC that can be applied across ad sponsors, campaigns, parties, and even countries. Understanding the extent of DDC is vital for policy discussions surrounding the regulation of microtargeting and data privacy.
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- PAR ID:
- 10568442
- Publisher / Repository:
- Cogitatio
- Date Published:
- Journal Name:
- Media and Communication
- Volume:
- 12
- ISSN:
- 2183-2439
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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