Blending Noisy Social Media Signals with Traditional Movement Variables to Predict Forced Migration

Singh, Lisa; Wahedi, Laila; Wang, Yanchen; Wei, Yifang; Kirov, Christo; Martin, Susan; Donato, Katharine M.; Liu, Yaguang; Kawintiranon, Kornraphop

doi:10.1145/3292500.3330774

Cited by 18 publications

(14 citation statements)

References 33 publications

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“…Therefore, it is not surprising that similar trends appear to be emerging when considering the prevalence of misinformation on social media surrounding COVID-19 [1,33,51,55]. While the type of misinformation being studied varies in these studies, all the studies show that misinformation sharing is occurring.…”

Section: Types and Frequency Of Shared Url Contentmentioning

confidence: 87%

“…Kousy and colleagues [33] used a random sample of tweets containing different coronavirus keywords and hashtags, and found that misinformation and unverifiable content within the tweets was being shared at a high rate, particularly by individual and informal group accounts (33.8%). Researchers have identified a number of conspiracy theories being shared [1,51,55], e.g., linking 5G to COVID-19, but the levels of sharing, the information cascades related to some of these conspiracies, and the belief in the conspiracy vary depending upon the user group studied and the specific conspiracy. [30] analyzed a set of misinformation claims identified by Google Fact Check Explorer and found that 88% of these claims were posted on social media sites, and that most of the information was recontextualization or 'spinning' of factual information.…”

Section: Types and Frequency Of Shared Url Contentmentioning

confidence: 99%

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Understanding high- and low-quality URL Sharing on COVID-19 Twitter streams

et al. 2020

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This article investigates the prevalence of high and low quality URLs shared on Twitter when users discuss COVID-19. We distinguish between high quality health sources, traditional news sources, and low quality misinformation sources. We find that misinformation, in terms of tweets containing URLs from low quality misinformation websites, is shared at a higher rate than tweets containing URLs on high quality health information websites. However, both are a relatively small proportion of the overall conversation. In contrast, news sources are shared at a much higher rate. These findings lead us to analyze the network created by the URLs referenced on the webpages shared by Twitter users. When looking at the combined network formed by all three of the source types, we find that the high quality health information network, the low quality misinformation network, and the news information network are all well connected with a clear community structure. While high and low quality sites do have connections to each other, the connections to and from news sources are more common, highlighting the central brokerage role news sources play in this information ecosystem. Our findings suggest that while low quality URLs are not extensively shared in the COVID-19 Twitter conversation, a well connected community of low quality COVID-19 related information has emerged on the web, and both health and news sources are connecting to this community.

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Section: Types and Frequency Of Shared Url Contentmentioning

confidence: 87%

Section: Types and Frequency Of Shared Url Contentmentioning

confidence: 99%

Understanding high- and low-quality URL Sharing on COVID-19 Twitter streams

et al. 2020

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“…For instance, if a conflict occurs in a specific region, this could be accurately measured by identifying users' reactions in that particular location via social media. In recent years, several universities and research centres have been working with big data in forecasting displacement globally, examining social media for sentiment analysis, and in evaluating economic and social variables (European Commission, 2017;Singh et al, 2019). In this sense, an ideal model could include behavioural and sentiment analysis collected online.…”

Section: Necessary Variables To Be Included In the Toolmentioning

confidence: 99%

The Role of Emerging Predictive IT Tools in Effective Migration Governance

Casagran

Boland

Sánchez-Montijano

et al. 2021

PaG

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Predicting mass migration is one of the main challenges for policymakers and NGOs working with migrants worldwide. Recently there has been a considerable increase in the use of computational techniques to predict migration flows, and advances have allowed for application of improved algorithms in the field. However, given the rapid pace of technological development facilitating these new predictive tools and methods for migration, it is important to address the extent to which such instruments and techniques engage with and impact migration governance. This study provides an in-depth examination of selected existing predictive tools in the migration field and their impact on the governance of migratory flows. It focuses on a comparative qualitative examination of these tools’ scope, as well as how these characteristics link to their respective underlying migration theory, research question, or objective. It overviews how several organisations have developed tools to predict short- or longer-term migration patterns, or to assess and estimate migration uncertainties. At the same time, it demonstrates how and why these instruments continue to face limitations that in turn affect migration management, especially as it relates to increasing EU institutional and stakeholder efforts to forecast or predict mixed migration. The main predictive migration tools in use today cover different scopes and uses, and as such are equally valid in shaping the requirements for a future, fully comprehensive predictive migration tool. This article provides clarity on the requirements and features for such a tool and draws conclusions as to the risks and opportunities any such tool could present for the future of EU migration governance.

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“…In [5], the authors provide short-term forecasts (two weeks ahead) for movements based on traditional indicators (e.g., socio-demographics) and social media sources such as news, Twitter, and event data. They derive roughly 400 features and use a hierarchical Bayesian model to provide probability densities, as opposed to a point forecast.…”

Section: Figurementioning

confidence: 99%

A machine learning approach to scenario analysis and forecasting of mixed migration

Nair¹,

Madsen²,

Lassen³

et al. 2020

IBM J. Res. & Dev.

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The development of MM4SIGHT, a machine learning system that enables annual forecasts of mixed-migration flows, is presented. Mixed migration refers to cross-border movements of people that are motivated by a multiplicity of factors to move including refugees fleeing persecution and conflict, victims of trafficking, and people seeking better lives and opportunity. Such populations have a range of legal status, some of which are not reflected in official government statistics. The system combines institutional estimates of migration along with in-person monitoring surveys to establish a migration volume baseline. The surveys reveal clusters of migratory drivers of populations on the move. Given macrolevel indicators that reflect migratory drivers found in the surveys, we develop an ensemble model to determine the volume of migration between source and host country along with uncertainty bounds. Using more than 80 macroindicators, we present results from a case study of migratory flows from Ethiopia to six countries. Our evaluations show error rates for annual forecasts to be within a few thousand persons per year for most destinations.

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Blending Noisy Social Media Signals with Traditional Movement Variables to Predict Forced Migration

Cited by 18 publications

References 33 publications

Understanding high- and low-quality URL Sharing on COVID-19 Twitter streams

Understanding high- and low-quality URL Sharing on COVID-19 Twitter streams

The Role of Emerging Predictive IT Tools in Effective Migration Governance

A machine learning approach to scenario analysis and forecasting of mixed migration

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