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"How can Twitter data be used to study individual-level human behavior and social interaction on a global scale? This book introduces readers to the methods, opportunities, and challenges of using Twitter data to analyze phenomena ranging from the number of people infected by the flu, to national elections, to tomorrow's stock prices. Each chapter, written by leading domain experts in clear and accessible language, takes the reader to the forefront of the newly emerging field of computational social science. An introductory chapter on Twitter data analysis provides an overview of key tools and skills, and gives pointers on how to get started, while the case studies demonstrate shortcomings, limitations, and pitfalls of Twitter data as well as its advantages. The book will be an excellent resource for social science students and researchers wanting to explore the use of online data"--

Abstract
In the wake of the COVID-19 pandemic, the International Organization for Migration has postulated that international migrant stocks fell short of their pre-pandemic projections by nearly 2 million as a result of travel restrictions. However, this decline is not testable with migration data from traditional sources. Key migration stakeholders have called for using data from alternative sources, including social media, to fill these gaps. Building on previous work using social media data to analyze migration responses to external shocks, we test the hypothesis that COVID-related travel restrictions reduced migrant stock relative to expected migration without such restrictions using estimates of migrants drawn from Facebook's advertising platform and dynamic panel models. We focus on four key origin countries in North and West Africa (Côte d'Ivoire, Algeria, Morocco, and Senegal) and on their 23 key destination countries. Between February and June 2020, we estimate that a destination country implementing a month-long total entry ban on arrivals from Côte d'Ivoire, Algeria, Morocco, or Senegal might have expected a 3.39% reduction in migrant stock from the restricted country compared with the counterfactual in which no travel restrictions were implemented. However, when broader societal disruptions of the pandemic are accounted for, we estimate that countries implementing travel restrictions might paradoxically have expected an increase in migrant stock. In this context, travel restrictions do not appear to have effectively curbed migration and could have resulted in outcomes opposite their intended effects.

Digital trace data presents an opportunity for promptly monitoring shifts in migrant populations. This contribution aims to determine whether the number of European migrants in the United Kingdom (UK) declined between March 2019 and March 2020, using weekly estimates derived from the Facebook Advertising Platform. The collected data is disaggregated according to age, level of education, and country of origin. To examine the fluctuation in the number of migrants, a simple Bayesian trend model is employed, incorporating indicator variables for age, education, and country. The Facebook data indicates a downward trend in the number of European migrants residing in the UK. This result is further confirmed by the data from the Labour Force Survey. Notably, the outcomes reveal that in the run-up to Brexit, the most significant decline occurred among the age group of 20 to 29 years old – the largest migrant group – and the tertiary educated. This analyses could not be implemented with traditional data sources such as the Labour Force Survey, because this level of disaggregation is not provided. However, there are also important limitations associated with digital trace data, such as algorithm changes and representativeness. These limitations need to be addressed by employing sound statistical methodologies. Nevertheless, this research shows the potential of digital trace data in anticipating migration trends at a timely granularity and informing policymakers.

Abstract
As online social activities have become increasingly important for people's lives, understanding how migrants integrate into online spaces is crucial for providing a more complete picture of integration processes. We curate a high-quality data set to quantify patterns of new online social connections among immigrants in the United States. Specifically, we focus on Twitter and leverage the unique features of these data, in combination with a propensity score matching technique, to isolate the effects of migration events on social network formation. The results indicate that migration events led to an expansion of migrants' networks of friends on Twitter in the destination country, relative to those of similar users who did not move. Male migrants between 19 and 29 years old who actively posted more tweets in English after migration also tended to have more local friends after migration compared to other demographic groups, indicating the impact of demographic characteristics and language skills on integration. The percentage of migrants' friends from their country of origin decreased in the first few years after migration and increased again in later years. Finally, unlike for migrants' friends' networks, which were under their control, we did not find any evidence that migration events expanded migrants' networks of followers in the destination country. While following users on Twitter in theory is not a geographically constrained process, our work shows that offline (re)location plays a significant role in the formation of online networks.

Although up-to-date information on the nature and extent of migration within the European Union (EU) is important for policymaking, timely and reliable statistics on the number of EU citizens residing in or moving across other member states are difficult to obtain. In this paper, we develop a statistical model that integrates data on EU migrant stocks using traditional sources such as census, population registers and Labour Force Survey, with novel data sources, primarily from the Facebook Advertising Platform. Findings suggest that combining different data sources provides near real-time estimates that can serve as early warnings about shifts in EU mobility patterns. Estimated migrant stocks match relatively well to the observed data, despite some overestimation of smaller migrant populations and underestimation for larger migrant populations in Germany and the United Kingdom. In addition, the model estimates missing stocks for migrant corridors and years where no data are available, offering timely now-casted estimates.

While the coronavirus disease 2019 (COVID-19) pandemic wreaked havoc across the globe, we have witnessed substantial mis- and disinformation regarding various aspects of the disease. We conducted a cross-sectional study using a self-administered questionnaire for the general public (recruited via social media) and healthcare workers (recruited via email) from the State of Qatar, and the Middle East and North Africa region to understand the knowledge of and anxiety levels around COVID-19 (April–June 2020) during the early stage of the pandemic. The final dataset used for the analysis comprised of 1658 questionnaires (53.0% of 3129 received questionnaires; 1337 [80.6%] from the general public survey and 321 [19.4%] from the healthcare survey). Knowledge about COVID-19 was significantly different across the two survey populations, with a much higher proportion of healthcare workers possessing better COVID-19 knowledge than the general public (62.9% vs. 30.0%, p < 0.0001). A reverse effect was observed for anxiety, with a higher proportion of very anxious (or really frightened) respondents among the general public compared to healthcare workers (27.5% vs. 11.5%, p < 0.0001). A higher proportion of the general public tended to overestimate their chance of dying if they become ill with COVID-19, with 251 (18.7%) reporting the chance of dying (once COVID-19 positive) to be ≥25% versus 19 (5.9%) of healthcare workers (p < 0.0001). Good knowledge about COVID-19 was associated with low levels of anxiety. Panic and unfounded anxiety, as well as casual and carefree attitudes, can propel risk taking and mistake-making, thereby increasing vulnerability. It is important that governments, public health agencies, healthcare workers, and civil society organizations keep themselves updated regarding scientific developments and that they relay messages to the community in an honest, transparent, unbiased, and timely manner.

AbstractIn times of crisis, real‐time data mapping population displacements are invaluable for targeted humanitarian response. The Russian invasion of Ukraine on February 24, 2022, forcibly displaced millions of people from their homes including nearly 6 million refugees flowing across the border in just a few weeks, but information was scarce regarding displaced and vulnerable populations who remained inside Ukraine. We leveraged social media data from Facebook's advertising platform in combination with preconflict population data to build a real‐time monitoring system to estimate subnational population sizes every day disaggregated by age and sex. Using this approach, we estimated that 5.3 million people had been internally displaced away from their baseline administrative region in the first three weeks after the start of the conflict. Results revealed four distinct displacement patterns: large‐scale evacuations, refugee staging areas, internal areas of refuge, and irregular dynamics. While the use of social media provided one of the only quantitative estimates of internal displacement in the conflict setting in virtual real time, we conclude by acknowledging risks and challenges of these new data streams for the future.