Migration has been proposed as one of the factors that shape cultural similarities across countries. However, studying the relationship between culture and migration has been challenging, in part because culture is difficult to quantify. The traditionally used survey questionnaires have a number of drawbacks, including that they are costly and difficult to scale to a large number of countries. To complement survey data, we propose the use of passively-collected digital traces from social media. We focus on food and drink as markers of a country’s culture. We then measure similarities between countries in terms of food and drink interests using aggregated data from the Facebook Advertising Platform. Methodologically, we offer approaches to measure the similarity between countries with both symmetric and asymmetric indices. Substantively, we assess the association between migration cultural similarity between countries by comparing our measure of cultural similarity with international migration data. In most countries, larger immigrant populations are associated with more similar food and drink preferences between their country of origin and the country of destination. Our results suggest that immigrants contribute to bringing the culture of their home countries to new countries. Moreover, our study identifies considerable variability in this pattern across countries.

WhatsApp is the most popular messaging app in the world. The closed nature of the app, in addition to the ease of transferring multimedia and sharing information to large-scale groups make What-sApp unique among other platforms, where an anonymous encrypted messages can become viral, reaching multiple users in a short period of time. The personal feeling and immediacy of messages directly delivered to the user's phone on WhatsApp was extensively abused to spread unfounded rumors and create misinformation campaigns during recent elections in Brazil and India. WhatsApp has been deploying measures to mitigate this problem, such as reducing the limit for forwarding a message to at most five users at once. Despite the welcomed effort to counter the problem, there is no evidence so far on the real effectiveness of such restrictions. In this work, we propose a methodology to evaluate the effectiveness of such measures on the spreading of misinformation circulating on WhatsApp. We use an epidemiological model and real data gathered from WhatsApp in Brazil, India and Indonesia to assess the impact of limiting virality features in this kind of network. Our results suggest that the current efforts deployed by WhatsApp can offer significant delays on the information spread, but they are ineffective in blocking the propagation of misinformation campaigns through public groups when the content has a high viral nature.

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Migration and mobility present major societal challenges, while also representing key opportunities. New data sources from social media, such as Facebook and LinkedIn, offer valuable insights that can help us measure and understand patterns of long-term migration and short-term mobility, as well as immigrant integration along various dimensions. In this chapter, we describe how Facebook data for advertisers can be used to quantify migration, and to study levels of assimilation of migrants based on the interests they express online. We use the term assimilation in a technical or neutral sense to express a measure of the distance between groups. In LinkedIn, aggregate-level information can be harvested to discover where migrants have studied, and where they live and work. In addition, all social media advertising data sources provide information about the languages that people speak (e.g., Polish speakers on Snapchat in London). Across all case studies, we discuss the limitations and risks of using these data, including privacy and legal concerns. Given the difficulty of collecting timely migration data, particularly for traits related to cultural assimilation, the methods we develop and the results we provide in this study open up new lines of research that computational social scientists are well-positioned to address.

O WhatsApp é um sistema de comunicação móvel que permite que pessoas interajam através de grupos. Neste trabalho, analisamos adisseminacão de informações dentro de uma rede de grupos que simula a rede do WhatsApp. A rede construída considera dois tipos de grupos: grupos orgânicos, formados por amigos e familiares, e grupos artificiais que, em geral, são criados com o objetivo de ser um meio de divulgação de determinado assunto ou evento, como por exemplo, campanhas políticas. Analisamos a velocidade com que se dá o espalhamento de informação nessa rede considerando o modelo epidemiológico Suscetível-Infectado (SI). Em seguida aprofundamos nossa análise buscando identificar parâmetros que fazem com que esse espalhamento seja parcialmente controlado de forma a dificultar a propagação de notícias falsas nessas redes. Nossos resultados quantificam a capacidade de viralização de um conteúdo no WhatsApp e identificam aspectos que poderiam limitar tal capacidade para evitar que a plataforma seja abusada em períodos eleitorais.

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