Tapping Into Advertising Data for Studying International Migration

Ingmar Weber is the Research Director of the Social Computing Group at the Qatar Computing Research Institute (QCRI). As an undergraduate Ingmar studied mathematics at Cambridge University, before pursuing a PhD at the Max-Planck Institute for Computer Science. He subsequently held positions at the Ecole Polytechnique Fédérale de Lausanne and Yahoo Research Barcelona. In his interdisciplinary research, he applies computational methods to large amounts of online data from social media and other sources to study human behaviour at scale. Particular topics of interest include quantifying international migration using digital methods and other data for development projects. He has published over 100 peer-reviewed articles and his work is frequently featured in popular press  Since 2016 he has been selected as an ACM Distinguished Speaker.

International migration is one of the key drivers of demographic change. However, official statistics on “stocks of migrants”, i.e. how many people with origin country X are residing in country Y, are often unreliable. Reasons for this include the free movement of EU nationals within the EU, as well as generally inadequate census and civil registration systems for many developing countries.

Work done by Emilio Zagheni, Krishna Gummadi and myself tries to address some of the shortcomings of traditional methods to create migration statistics by tapping into a new kind of data: audience estimates provided by Facebook.

Facebook and other internet giants collect a rich data set on their users to be able to serve more targeted and more relevant advertising to their users. The data collected includes user self-declared attributes such as age or gender, it includes meta data such as the device or internet connection type used to access the service, it includes third party information such as credit card or voter registration data, and it includes attributes such as topical interests inferred from behavior such as "liking" posts on Facebook or visiting websites with social plugins. See https://www.cision.com/us/2017/07/how-to-improve-social-media-targeting/ for a good list of available targeting options on Facebook, Twitter, LinkedIn and Snapchat.

 

The detailed users profiles are generally not available to researchers outside the companies. However, aggregate and anonymized data is shared with potential advertisers in the form of audience estimates. Basically, Facebook and other social networks provide advertisers with information on "how many users match criteria X". For example, to help with planning an advertising campaign, an advertiser could inquire "how many monthly active Facebook users are married, male German expats aged 30-50 living in Qatar"? Answer: 120 (as of Dec 20, 2017).

This type of real-time digital census over Facebook's could potentially be of value to augment existing population estimates, in particular for countries where official statistics are unreliable or outdated. However, due to selection biases and an estimated 13% of duplicate or fake accounts it is clear that using this data set as a simplistic enumeration tool for the whole population will not give accurate results. See https://www.theguardian.com/technology/2017/sep/07/facebook-claims-it-can-reach-more-people-than-actually-exist-in-uk-us-and-other-countries for more indications of shortcomings of the data.

In our own research, we do not use the raw advertising audience estimates as the final answer. Rather we treat it as one of potentially many input signals for an estimation task of the kind "how many Germans are living in Qatar today"? As long as the biases in the underlying data are either (i) uniform, e.g. 13% of duplicate or fake Facebook accounts for all countries, or (ii) systematic, e.g. Western Europeans are always less likely to be on Facebook compared to Arab nationals, an appropriately fitted model can account for and correct such biases.

In our paper “Leveraging Facebook'sAdvertising Platform to Monitor Stocks of Migrants”, Emilio, Krishna Gummadi and I show the feasibility of this approach to derive stocks of migrants across different US states and around the world. Concretely, we show that it is indeed possible to build models to make out-of-sample predictions on how many people from a certain origin country are residing in a particular US state. Similarly, it is possible to predict the percentage of expats out of the whole population for countries around the globe.

Potentially, the Facebook audience estimates could also give estimates for stocks of migrants at the sub-national and even the sub-city level. To illustrate this, Matheus Araujo, Michael Aupetit, Yelena Mejova and myself created a data visualization for the Facebook data for Doha: http://fb-doha.qcri.org.

As an example, this shows a density map of Nepali expats across Doha, with the highest density in the Industrial Area. The tool also shows that Nepali expats in Doha are predominantly male (93%) and are Android users (94%). Contrast this to the same map for Western expats with the highest densities in West Bay and the Pearl. Western expats are more gender balanced (44% female) and more likely to own iPhones (56%).  A similar visualization for New York City can be explored at http://fb-nyc.qcri.org [Usage info for the two data visualizations: Select several filters on the left to drill down to smaller populations by nationality, gender or other criteria. Click a selection again to de-select and revert to the whole category such as all nationalities or all genders.]

Given Facebook’s global reach of 2.1B monthly active users we believe there is a lot of potential in using this data source to support global development efforts, in particular given its easy accessibility through official APIs. At the same time, no single data source is a cure-all and many have complementary strengths. Satellite data has truly global reach and can give estimates of population densities but satellite data will never reveal the nationality or gender of earthlings. Call detail records (CDR, https://en.wikipedia.org/wiki/Call_detail_record) are great for studying dynamic changes in population density, but there are limitations for monitoring international migration as people often change their SIM cards once they move.

I’m truly optimistic that as Digital Demography advances and matures as a field and as researchers start to work collaboratively, combining different data sources, we will see more and more scientific work with real impact on the creation of migration statistics. If you’re interested in how to use new data sources and methodologies to help fill data gaps around the globe, please get in touch by email at: iweber -atsignal - hbku.edu.qa.

 

Relevant slide decks:

Using internet advertising data for studying international migration (https://www.slideshare.net/IngmarWeber/using-internet-advertising-data-for-studying-international-migration)

Digital Demography - WWW'17 Tutorial - Part II (https://www.slideshare.net/IngmarWeber/digital-demography-www17-tutorial-part-ii)

Wrapper libraries to obtain Facebook advertising audience estimates:

Wrapper library in R (https://github.com/CSDE-UW/IUSSP-digital-demog-2017) by Connor Gilroy (https://soc.washington.edu/people/connor-gilroy)

Wrapper library in Python (https://github.com/maraujo/pySocialWatcher) by Matheus Araujo (https://sites.google.com/view/matheusaraujo/)

All of my publications are available at https://ingmarweber.de/publications/. Feel free to follow me at https://twitter.com/ingmarweber.

Gender stereotyping used in the media for the recruitment of women to the British Army

This blog is written by Paige Salvage, a student at Cardiff University. She has been studying relevant sociological theories and methods for how we can understand the digital society and how the Internet shapes our everyday lives.

 

The recent development of government legislation allowing female soldiers into infantry positions has resulted in extensive sociological interest in the changing role of women in the Army. Stiehm (1988) suggested that women in the Army have fixed stereotypes, “whores or lesbians” suggesting that serving female soldiers have both sexual and sexuality foci. Assuming this is true, Goffman’s (1976:9) suggests that “gender displays […] reflect fundamental features of the social structure” in the Army. This study considered how images that have been used across social media, in particular the British Army’s Instagram page (@britisharmy), depict women who are already serving. In order to interpret the findings it was important to understand how women are stereotyped into roles society deem as typically ‘female’.

It is important to note that Stiehm’s (1988) stereotypical labelling of women in the Army is not only subjective but also unethical. However, Goffman’s (1976) work on gender displays balanced any possible disparity as he suggested that gender is a flexible and fluid notion. With Braun and Clarke’s (2006) guide to thematic analysis, this study aimed to probe the following two inductive themes:

1.       Masculine - those who are taking on a more masculine role or are taking part in a military activity that is stereotypically male      

2.       Feminine - those who take part in more stereotypically female activities

 

Data collection was limited to the British Army’s Instagram page (@britisharmy) after 8^th July 2016 when women were officially allowed access to infantry roles. This period was considered significant as it was expected that more images of women in combat or infantry scenarios would be shared as a promotion of women into these roles.

Findings

Masculine

 

Analysis of images in Instagram (@britisharmy) suggested that images of women are more likely to be depicted in masculine roles (Figure 1). This suggests that the British Army are attempting to break the down the assumptions that women in the military are the weaker sex. By providing the public with images of women taking part in arduous and dangerous activities, the British Army are promoting masculine roles for women.

However, this does not address the common stereotypes that depict women in the Army. Women who hold a more masculine physique in these images may be considered “lesbian”, as Stiehm (1988) noted that this label was linked more towards physical attributes rather than the sexuality of the individual. As some show masculine traits, these women become ‘othered’ by assuming a more male stereotype.

 

Feminine

There are significantly less images in instagram depicting women in the army as feminine (Figure 2), suggesting that the British Army are biased towards using images to portray women in active combat roles. This is contrary to the acknowledged social role of women as homemakers and carers. Indeed, feminine images of women on @britisharmy tend to depict women in healthcare or domestic (eg chefs) roles, suggest that the British Army concur with the idea that women need less physically demanding  roles.

From this study, we can start to understand how masculine pictures of women are used as promotional aids to increase recruitment numbers, particularly into infantry roles. It appears that, following the 2016 policy change to allow females into infantry regiments, the British Army are now targeting women in their recruitment campaigns. However, it could not be concluded that female stereotypes were purposefully used to recruit certain types of women for these infantry roles.