Getting the Message Out: Social Media and Word-of-Mouth as Effective Communication Methods during Emergencies
Bibliographic record
Abstract
Effective communication is a critical part of managing an emergency. During an emergency, the ways in which health agencies normally communicate warnings may not reach all of the intended audience. Not all communities are the same, and households within communities are diverse. Because different communities prefer different communication methods, community leaders and emergency planners need to know their communities' preferred methods for seeking information about an emergency. This descriptive report explores findings from previous community assessments that have collected information on communication preferences, including television (TV), social media, and word-of-mouth (WoM) delivery methods. Data were analyzed from 12 Community Assessments for Public Health Emergency Response (CASPERs) conducted from 2014-2017 that included questions regarding primary and trusted communication sources. A CASPER is a rapid needs assessment designed to gather household-based information from a community. In 75.0% of the CASPERs, households reported TV as their primary source of information for specific emergency events (range = 24.0%-83.1%). Households reporting social media as their primary source of information differed widely across CASPERs (3.2%-41.8%). In five of the CASPERs, nearly one-half of households reported WoM as their primary source of information. These CASPERs were conducted in response to a specific emergency (ie, chemical spill, harmful algal bloom, hurricane, and flood). The CASPERs conducted as part of a preparedness activity had lower percentages of households reporting WoM as their primary source of information (8.3%-10.4%). The findings in this report demonstrate the need for emergency plans to include hybrid communication models, combining traditional methods with newer technologies to reach the broadest audience. Although TV was the most commonly reported preferred source of information, segments of the population relied on social media and WoM messaging. By using multiple methods for risk communication, emergency planners are more likely to reach the whole community and engage vulnerable populations that might not have access to, trust in, or understanding of traditional news sources. Multiple communication channels that include user-generated content, such as social media and WoM, can increase the timeliness of messaging and provide community members with message confirmation from sources they trust encouraging them to take protective public health actions.WolkinAF, SchnallAH, NakataNK, EllisEM. Getting the message out: social media and word-of-mouth as effective communication methods during emergencies. Prehosp Disaster Med. 2019;34(1):89-94.
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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 | 0.014 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".