ReviewPeerJ. Computer science2024
Word embedding for social sciences: an interdisciplinary survey.
Review in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Quantifying the influence of vocational education and training with text embedding and similarity-based networks.PloS one · 2025Article
- Article
- Nature is resource, playground, and gift: What artificial intelligence reveals about human-Nature relationships.PloS one · 2024Article
- Word embedding for social sciences: an interdisciplinary survey.PeerJ. Computer science · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning models learn low-dimensional representations from complex high-dimensional data. Not only computer science but also social science has benefited from the advancement of these powerful tools. Within such tools, word embedding is one of the most popular methods in the literature. However, we have no particular documentation of this emerging trend because this trend overlaps different social science fields. To well compile this fragmented knowledge, we survey recent studies that apply word embedding models to human behavior mining. Our taxonomy built on the surveyed article provides a concise but comprehensive overview of this emerging trend of intersection between computer science and social science and guides scholars who are going to navigate the use of word embedding algorithms in their voyage of social science research.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.