Social media recruitment and online data collection: A beginner’s guide and best practices for accessing low-prevalence and hard-to-reach populations.
Bibliographic record
Abstract
One facet of the growing social media phenomenon is the opportunity to directly appeal to prospective research participants. An example of this is Facebook advertising to defined populations. In conjunction with online data collection, social media advertising can simplify and accelerate data collection, and it can do so at greatly reduced costs. Thanks to these contemporary tools, responses can be collected at the same time from participants living in Vancouver, Toronto, and St. John's. In this article, we describe how social media can be used for rapid and cost-effective data collection. Moreover, these methods allow researchers to directly access prospective study participants who may be otherwise difficult to reach (because of their low prevalence, their remote location, or organisational barriers). For illustrative purposes, we review methods from 2 studies: 1 of older adults with bipolar disorder and 1 of Canadian paramedics and their spouses. In both cases, participants clicked sociodemographically targeted Facebook advertisements and were directed to online study questionnaires. Based primarily on these 2 lines of research, we offer recommendations and best practices for researchers interested in utilizing social media for online recruitment and data collection. We contend that in many instances, social media may be the most effective means to recruit participants from low-prevalence and invisible populations. The majority of Canadians, and indeed much more of the world population than was previously accessible, can be reached via social media today. In addition to offering strategies to improve participant communication, we also review the limitations of social media advertising and online research.Keywords: participant recruitment, data collection, technology, social media, FacebookResumeLa possibilite de solliciter directement des sujets potentiels pour la recherche est F un des avantages du phenomene croissant des medias sociaux. Sur Facebook, la publicite ciblant certains segments de la population en constitue un exemple. Parallelement a la collecte de donnees en ligne, la publicite diffusee sur les medias sociaux peut simplifier et accelerer le processus de collecte de donnees, cela a un cout beaucoup moindre. Grâce a ces outils modernes, il est possible de recevoir simultanement les reponses de repondants habitant a Vancouver, a Toronto et a St. John's. Dans cet article, nous expliquons la facon d'utiliser les medias sociaux pour effectuer une collecte de donnees rapide, efficace et peu onereuse. De plus, ces methodes permettent aux chercheurs de communiquer directement avec des participants aux etudes prospectives qui seraient difficiles a joindre autrement - en raison d'une faible prevalence, de leur lieu d'habitation dans une region eloignee ou d'obstacles organisationnels. A titre d'information, nous examinons les methodes employees dans deux etudes : une consacree aux adultes âges presentant un trouble bipolaire, et l'autre consacree au personnel paramedical canadien et a leurs epouses ou epoux. Dans les deux cas, les repondants ont clique sur les annonces de Facebook ciblees sur le plan sociodemographique et ont ete diriges vers des questionnaires d'etude. En nous basant principalement sur ces deux domaines de recherche, nous presentons des recommandations et des pratiques exemplaires aux chercheurs souhaitant utiliser les medias sociaux pour effectuer un recrutement et une collecte de donnees en ligne. Nous estimons que, dans de nombreux cas, les medias sociaux constituent le moyen le plus efficace de recruter des sujets au sein de segments de population a faible prevalence et meconnus. Aujourd'hui, la plupart des Canadiens - et, bien entendu, une plus grande partie de la population mondiale qu'auparavant - peuvent etre joints par l'intermediaire des medias sociaux. En plus de proposer des strategies pour ameliorer la communication avec les repondants, nous etudions les limites de la publicite sur les medias sociaux et celles de la recherche en ligne. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.118 | 0.166 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.025 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".