When the Firehose Runs Dry: Crisis Researchers Scramble to Replace Twitter Data

For more than a decade, the flow of short, timestamped posts from Twitter was the lifeblood of computational crisis research. When floods, earthquakes, wildfires or epidemics struck, researchers and emergency managers could tap a public stream of crowdsourced reports in near real time, using them to build situational awareness and guide decision-makers during mass emergencies. That era is now effectively over. A new opinion article published in PLOS Complex Systems by Roberto Interdonato and colleagues at the TETIS research unit in Montpellier, France, argues that restricted access to social media data is forcing the field into a fundamental rethink, away from dependence on a single dominant platform and toward multi-source monitoring frameworks that combine online news media, radio broadcasts and a patchwork of alternative social networks.

The authors trace how social media became the default data source for crisis informatics. Following the massive diffusion of platforms during the first decade of the 2000s, it became clear that they could act as crowdsourced newsfeeds on virtually any topic. Among the options, Twitter quickly emerged as the most suitable for research, thanks to its micro-blogging format of frequent short posts and, crucially, the availability of user-friendly application programming interfaces. Even though full access required a subscription fee, the free tier of the APIs allowed enough data to be collected to support work across many domains. Rival platforms never achieved the same standing in the scientific community: Facebook was difficult to access, while YouTube, Instagram and Flickr posed challenges for processing multi-modal content with the methodologies available at the time.

The analytical toolkit built around Twitter data relied heavily on network analysis to explore community structures and information diffusion, alongside natural language processing to identify crisis events and assess public perception. Yet researchers have long documented the limitations of these data, including demographic and other selection biases among users, technical difficulties posed by the volume, unstructured nature and multilingualism of the content, and the overflow of unverified material that facilitates the spread of misinformation. More recently, the integration of transformer-based architectures and large language models has marked a significant shift in crisis monitoring. BERT-based models have dominated event detection and classification tasks, while decoder-only models such as GPT-4 and LLaMA-2 have gained traction for their reasoning and few-shot capabilities. Even so, the authors note, these models still struggle with the informal language and domain-specific jargon characteristic of crisis communication.

The turning point came with Elon Musk’s acquisition of Twitter in 2022 and the operational changes that followed: the rebranding to X, altered content moderation policies, and, most consequentially for scientists, the end of the free API tier in February 2023. These developments prompted many researchers and practitioners to reconsider their reliance on the platform for crisis communication, marking what the authors describe as a potential end to the era when Twitter served as a primary conduit for scientific discourse during public emergencies. In the wake of these events, alternative micro-blogging platforms such as Bluesky, Mastodon and Threads have emerged and attracted large migrations of users. However, considerable uncertainty remains about when, or even whether, some of these networks will reach the critical mass needed to make them useful for crisis management.

A bibliometric analysis conducted by the team underscores the scale of the disruption. Counting Scopus-indexed articles whose titles mention a specific online social network alongside a crisis management keyword, restricted to four data-oriented subject areas, the authors chart the rise and subsequent decline of platform-specific crisis research. The pattern reflects a broader cooling of enthusiasm for social media data in the field, driven not only by access costs but also by a second, quieter constraint: regulation.

Since May 2018, the European Union’s General Data Protection Regulation has set the terms for how personal data can be collected, stored and reused, and the authors argue it remains the only example of a unified privacy law at continental scale. While the GDPR is now a fundamental component of EU privacy and human rights law, it also represents a significant limitation on how social media data can be used in research projects, particularly EU-funded ones such as Horizon and ERC grants. Reproducibility suffers because researchers are often unable to archive and share datasets containing information that could directly or indirectly identify individuals. Moreover, when users withdraw their consent from a platform, all corresponding records must be removed from datasets, which implies monitoring systems capable of detecting consent withdrawal events such as account deletions and updating datasets accordingly. Over time, as more users delete accounts or revoke consent, datasets shrink, further complicating longitudinal analyses. The authors also raise concerns that Twitter/X’s new policies may no longer fully satisfy key GDPR obligations, and they point to limited but suggestive evidence that people may nonetheless agree to share personal data during emergencies.

What, then, can replace the Twitter firehose? The authors examine the leading alternatives and find that none can fully substitute for the platform’s former role in disaster management. TikTok lacks a real-time public stream. Reddit and Telegram offer only fragmented access, with content dispersed across independent subreddits and channels that must each be individually identified and monitored rather than aggregated into a single feed. Bluesky and Mastodon suffer from extremely limited adoption. These constraints position the alternative platforms as valuable complements rather than standalone replacements, and the authors stress that each platform must be matched to its audience and user behaviors, and to the regional contexts and analytical objectives of a given crisis.

The core of the article’s argument is a call for multi-source crisis monitoring frameworks that combine online news media and radio broadcasts with whatever social media data remain accessible. Beyond improving coverage, such diversification reduces the selection biases inherent to any single source by capturing heterogeneous communities and communication practices. Media-based sources carry their own editorial and political biases, but they offer complementary advantages. Local radio stations in particular remain key information channels in low-connectivity contexts and provide access to under-represented community-level perceptions that are largely invisible on global online platforms; recent work has used machine learning to analyze radio broadcasts about the Ebola outbreak in Uganda, for example. When combined with national and international press, these sources partially compensate for selection biases and contribute to more geographically equitable situational awareness. Compared with user-generated content, media sources also allow better control of misinformation through source filtering and credibility assessment, and they raise fewer legal and ethical challenges under data protection regimes such as the GDPR. The authors argue that collective efforts are needed to establish governance frameworks enabling controlled access to social media data during crises, so that such data can be integrated into operational decision-making.

Moving to multi-source infrastructures introduces new technical problems of its own. The same events are often reported across multiple channels, requiring robust deduplication and information fusion methods to avoid redundancy and contradictory signals. Multilingual processing remains a major bottleneck, especially for under-resourced languages prevalent in many crisis-prone regions; Africa alone has more than 2,000 indigenous languages, and dedicated multilingual models will be needed to handle them. Without focused work on low-resource natural language processing, the authors warn, multi-source systems risk reproducing existing geographical and linguistic inequities in crisis response. Their conclusion is that future crisis management systems must integrate a broad panel of data, including multimodal and multilingual content, while tackling two central challenges: the fusion and ingestion of heterogeneous data, and the mitigation of biases, which generative AI can reproduce and amplify, potentially entrenching long-standing inequalities. The era of one platform, one feed and one methodology is ending, and what replaces it will determine how quickly the world can see a disaster unfolding.

Subject of Research: The impact of restricted social media access and data protection regulation on crisis management research and the shift toward multi-source crisis monitoring

Article Title: Rethinking data sources for crisis management: How restricted social media access is reshaping current practices

Article References: Interdonato, R., Decoupes, R., Roche, M., Syed, M. A., Teisseire, M., & Valentin, S. (2026). Rethinking data sources for crisis management: How restricted social media access is reshaping current practices. PLOS Complex Systems, 3(6), e0000112. https://doi.org/10.1371/journal.pcsy.0000112

Image Credits: AI Generated

DOI: 10.1371/journal.pcsy.0000112

Keywords: crisis management, social media data, Twitter/X API, GDPR, disaster monitoring, natural language processing, large language models, misinformation, multilingual NLP, online news media, radio broadcasts, data fusion