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Sense-Giving Strategies of Media Organisations in Social Media Disaster Communication: Findings from Hurricane Harvey

Published 18 Apr 2020 in cs.SI and cs.CY | (2004.08567v1)

Abstract: Media organisations are essential communication stakeholders in social media disaster communication during extreme events. They perform gatekeeper and amplification roles which are crucial for collective sense-making processes. In that capacity, media organisations distribute information through social media, use it as a source of information, and share such information across different channels. Yet, little is known about the role of media organisations on social media as supposed sense-givers to effectively support the creation of mutual sense. This study investigates the communication strategies of media organisations in extreme events. A Twitter dataset consisting of 9,414,463 postings was collected during Hurricane Harvey in 2017. Social network and content analysis methods were applied to identify media communication approaches. Three different sense-giving strategies could be identified: retweeting of local in-house outlets; bound amplification of messages of individual to the organisation associated journalists; and open message amplification.

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Summary

  • The paper identifies three core sense-giving strategies: retweeting local outlets, bound amplification, and open message amplification.
  • The paper uses social network and content analysis on a dataset of over 9 million tweets to evaluate media behaviors during the crisis.
  • The paper demonstrates that media sense-giving enhances collective sense-making and bridges communication among diverse stakeholders in disasters.

Sense-Giving Strategies of Media Organisations in Social Media Disaster Communication

Introduction

The research paper titled "Sense-Giving Strategies of Media Organisations in Social Media Disaster Communication: Findings from Hurricane Harvey" (2004.08567) presents an empirical study on the communication strategies employed by media organizations on social media during disaster events, specifically Hurricane Harvey in 2017. The study aims to elucidate the role of media organizations in collective sense-making by exploring how these entities distribute and amplify crisis information on platforms like Twitter.

Literature Context

Crisis communication research increasingly acknowledges Twitter as a pivotal medium for real-time information exchange during disasters. Previous studies have primarily focused on Emergency Management Agencies (EMAs) and their roles in crisis information dissemination. However, this paper shifts the focus to media organizations, addressing a notable research gap regarding their function as sense-givers. The concept of sense-giving, derived from organizational theory, is extended to the public domain, providing a framework to understand the influence media organizations wield during crises.

Methodology

The study deploys a comprehensive data analysis approach, leveraging a Twitter dataset comprising 9,414,463 posts collected over a six-day period during Hurricane Harvey. The methodology integrates social network analysis and content analysis to assess both the influence and strategic behaviors of media organizations on Twitter. The dataset is dissected into daily segments to facilitate manageable data handling and align with analytic tool requirements.

Findings

The analysis identifies three primary sense-giving strategies employed by media organizations:

  1. Retweeting Local In-house Outlets: National media outlets leverage the reach of localized branches through retweets, capitalizing on their regional authority to disseminate information effectively.
  2. Bound Amplification: Media organizations amplify messages by retweeting associated journalists, ensuring content quality and trustworthiness. This approach was exemplified by local outlets like the Houston Chronicle, which extensively retweeted its own journalists' reports.
  3. Open Message Amplification: Encouraging contributions from the general public through hashtags or direct mentions, media organizations like ABC13 Houston incorporated eyewitness accounts into their network, broadening informational coverage.

Discussion

The paper discerns that media organizations not only produce a significant volume of unique content but also play a crucial bridging role in connecting various stakeholders within the social media landscape. The findings underscore the critical function of media organizations as authoritative sense-givers, significantly influencing the public's understanding during crises. The strategies identified facilitate enhanced information dissemination, potentially improving crisis response efforts.

Conclusion

This study contributes to the discourse on disaster communication by highlighting the pivotal role of media organizations as sense-givers on social media. It reveals how media strategies can enhance collective sense-making, offering practical insights applicable to both EMAs and media bodies. Future research could explore the challenges faced by media organizations in unpredictable crises and extend the study to other social media platforms, further enriching the understanding of media's role in crisis contexts.

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Knowledge Gaps

Below is a single, consolidated list of concrete knowledge gaps, limitations, and open questions left unresolved by the paper that future researchers could act on:

  • Platform scope: Findings are Twitter-only; the cross-platform dynamics with Facebook, Instagram, YouTube, Reddit, and broadcast/website channels remain unexamined.
  • Temporal coverage: Data covers only six days (Aug 26–31, 2017); impacts of pre-event buildup and long-tail recovery phases are missing.
  • Crisis typology generalizability: Results stem from a predictable, weather-forecasted disaster; it is unclear if identified strategies transfer to sudden, unpredictable crises (e.g., terror attacks, earthquakes).
  • Language and geography: English-only collection likely excludes significant communities; international media were absent in the power-user sample—are strategies different across languages/regions?
  • Keyword dependence: Using only “hurricaneharvey,” “harvey,” “hurricane” risks both missing relevant hashtags/terms (e.g., #HoustonFlood, #HarveyRelief, local place names) and including noise; sensitivity analyses are needed.
  • Data completeness and reproducibility: The paper does not specify API endpoints, rate-limit handling, or sampling bias checks; the dataset is not shared for replication.
  • Retweet-centric influence: Influence is approximated via retweet in-degree; quote tweets, replies, mentions, impressions, follower networks, and algorithmic feed exposure are not incorporated.
  • Power-user sampling bias: Focusing on top 100 power users per day and on media/journalists neglects the “long tail,” potentially missing grassroots sense-giving and niche authorities.
  • Network modeling limits: Analyses model only retweet graphs; reply/mention networks, community structures (e.g., modularity), structural holes, and cross-community bridging are not explored.
  • Temporal diffusion dynamics: No modeling of time-resolved diffusion cascades (who amplifies whom, when), nor of lag between local and national outlets.
  • Measurement validity: Betweenness and in-degree are reported without robustness checks (e.g., alternative centralities, bootstrapping) or tests of statistical significance.
  • Strategy effectiveness: The study identifies three amplification strategies but does not evaluate their effects on outcomes (e.g., faster information reach, reduced uncertainty, behavioral compliance, rumor suppression).
  • Content accuracy and verification: Accuracy/credibility of amplified content—especially in open amplification—was not assessed; assumptions about “bound amplification = quality assurance” remain untested.
  • Misinformation dynamics: The role of media strategies in amplifying, containing, or correcting rumors and falsehoods is not examined.
  • Organizational processes: Internal decision rules, editorial policies, staffing, tooling (dashboards, verification workflows), and resource constraints driving strategy choices are unknown.
  • National–local coordination: Observed asymmetries (e.g., ABC News not retweeting @abc13houston) are not explained; incentives, brand risk, and policy constraints require investigation.
  • Audience reception and trust: Public perceptions (trust, sentiment), engagement quality, and the impact on sense-making among different audience segments are not measured.
  • Bot/coordinated activity: Potential effects of bots, coordinated campaigns, or harassment on amplification patterns and media behavior are not assessed.
  • Geospatial granularity: No geolocation analysis of content or of reporter distribution; spatial coverage and information equity across affected neighborhoods are not evaluated.
  • Hashtag solicitation risks: The #ABC13Eyewitness call-to-action’s susceptibility to spam, malicious content, privacy/safety concerns, and moderation workload is not analyzed.
  • Theoretical operationalization: Sense-giving is invoked but not operationally measured; interactions with sense-demanding and sense-breaking are not modeled or tested.
  • Comparative scope: A single US case limits external validity; cross-event, cross-hazard, and cross-country comparisons are needed to test strategy universality.
  • EMA applicability: The recommendation that EMAs emulate media strategies is speculative; controlled evaluations are needed to test feasibility, benefits, and risks for EMAs.
  • Post-2017 platform changes: Twitter’s product/policy shifts (e.g., algorithmic timeline changes, verification policies, API access) may affect replicability; longitudinal reassessment is needed.
  • Ethical considerations: The use and rebroadcast of eyewitness content raises consent, safety, and equity issues that are not addressed.
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