Yonas Mitike Kassa

Bio: Yonas Mitike Kassa is an academic researcher from Charles III University of Madrid. The author has contributed to research in topics: Digital divide & Social media. The author has an hindex of 3, co-authored 5 publications receiving 54 citations. Previous affiliations of Yonas Mitike Kassa include IMDEA.

Analyzing gender inequality through large-scale Facebook advertising data.

Abstract: Online social media are information resources that can have a transformative power in society. While the Web was envisioned as an equalizing force that allows everyone to access information, the digital divide prevents large amounts of people from being present online. Online social media, in particular, are prone to gender inequality, an important issue given the link between social media use and employment. Understanding gender inequality in social media is a challenging task due to the necessity of data sources that can provide large-scale measurements across multiple countries. Here, we show how the Facebook Gender Divide (FGD), a metric based on aggregated statistics of more than 1.4 billion users in 217 countries, explains various aspects of worldwide gender inequality. Our analysis shows that the FGD encodes gender equality indices in education, health, and economic opportunity. We find gender differences in network externalities that suggest that using social media has an added value for women. Furthermore, we find that low values of the FGD are associated with increases in economic gender equality. Our results suggest that online social networks, while suffering evident gender imbalance, may lower the barriers that women have to access to informational resources and help to narrow the economic gender gap.

Facebook's gender divide

Abstract: Online social media are information resources that can have a transformative power in society. While the Web was envisioned as an equalizing force that allows everyone to access information, the digital divide prevents large amounts of people from being present online. Online social media in particular are prone to gender inequality, an important issue given the link between social media use and employment. Understanding gender inequality in social media is a challenging task due to the necessity of data sources that can provide unbiased measurements across multiple countries. Here we show how the Facebook Gender Divide (FGD), a metric based on a dataset including more than 1.4 Billion users in 217 countries, explains various aspects of worldwide gender inequality. Our analysis shows that the FGD encodes gender equality indices in education, health, and economic opportunity. We find network effects that suggest that using social media has an added value for women. Furthermore, we find that low values of the FGD precede the approach of countries towards economic gender equality. Our results suggest that online social networks, while suffering evident gender imbalance, may lower the barriers that women have to access informational resources and help to narrow the economic gender gap.

A Large-Scale Analysis of Facebook’s User-Base and User Engagement Growth

Abstract: Understanding the evolution of the user base as well as the user engagement of online services is critical not only for the service operators but also for customers, investors, and users. While we can find research works addressing this issue in online services, such as Twitter, MySpace, or Google+, such detailed analysis is missing for Facebook, which is currently the largest online social network. This paper presents the first detailed study on the demographic and geographic composition and evolution of the user base and user engagement in Facebook over a period of three years. To this end, we have implemented a measurement methodology that leverages the marketing API of Facebook to retrieve actual information about the number of total users and the number of daily active users across 230 countries and age groups ranging between 13 and 65+. The conducted analysis reveals that Facebook is still growing and geographically expanding. Moreover, the growth pattern is heterogeneous across age groups, genders, and geographical regions. In particular, from a demography perspective, Facebook shows the lowest growth pattern among adolescents. Gender-based analysis showed that growth among men is still higher than the growth in women. Our geographical analysis reveals that while Facebook growth is slower in western countries, it has the fastest growth in the developing countries mainly located in Africa and Central Asia; analyzing the penetration of these countries also shows that these countries are at earlier stages of Facebook penetration. Leveraging external socioeconomic datasets, we also showed that this heterogeneous growth can be characterized by indicators, such as availability and access to Internet, Facebook popularity, and factors related with population growth and gender inequality.

The Value of Online Users: Empirical Evaluation of the Price of Personalized Ads

Abstract: Ad networks use the behaviors of online users to associate them with preferences (features), and market these features to enable advertisers to target online users. Typical features associated with users include location, interests, gender, age, and etc. Furthemore, ad networks provide their clients with campaing creation tools to help to them to configure and run campains. In this paper, we study the pricing of ads using the ad campaing planning tools of ad networks. We develop tools to collect the suggested bid prices from two platforms: YouTube and Facebook. Analyzing these prices we find that United States is the most expensive country in both platforms. We also find that the most expensive preferences are different in YouTube and Facebook. In YouTube, the top preferences are related to Oil & Gas, while in Facebook are devices, ethnics or politics depending on the type of bidding. Finally, we do not find any price difference genders in Facebook.

Optimization of Soft Mobility Localization with Sustainable Policies and Open Data

Abstract: A quarter of global greenhouse emissions come from transport, with modern cities producing more than 60% of these emissions. To reduce carbon footprint, several solutions on soft mobility (e.g., optimizing electric vehicles locations) have been proposed using IoT resources and AI techniques. However, these solutions either lack replicability since they ignore city’s needs per area and economic restrictions or lack algorithmic fairness since they account no social criteria (e.g., disabled, age, gender). In this work, we developed AI-based methods to automatically detect the different areas (e.g., rural, urban) and propose two heuristics which incorporate social, environmental and economic criteria of the area in their decision making in the form of sustainability policy templates. Our heuristics solve the p-median problem; they minimize the distance of stations to important points constrained by the cost of new stations. We show that our proposed solution is able to disperse the new stations within the city while covering local neighbourhoods. This work is replicated in two big European cities adapted to different open data and demonstrated by a dedicated visual dashboard.

Media Bias Monitor: Quantifying Biases of Social Media News Outlets at Large-Scale

Abstract: As Internet users increasingly rely on social media sites like Facebook and Twitter to receive news, they are faced with a bewildering number of news media choices. For example, thousands of Facebook pages today are registered and categorized as some form of news media outlets. Inferring the bias (or slant) of these media pages poses a difficult challenge for media watchdog organizations that traditionally rely on content analysis. In this paper, we explore a novel scalable methodology to accurately infer the biases of thousands of news sources on social media sites like Facebook and Twitter. Our key idea is to utilize their advertiser interfaces, that offer detailed insights into the demographics of the news source’s audience on the social media site. We show that the ideological (liberal or conservative) leaning of a news source can be accurately estimated by the extent to which liberals or conservatives are over-/under-represented among its audience. Additionally, we show how biases in a news source’s audience demographics, along the lines of race, gender, age, national identity, and income, can be used to infer more fine-grained biases of the source, such as social vs. economic vs. nationalistic conservatism. Finally, we demonstrate the scalability of our approach by building and publicly deploying a system, called "Media Bias Monitor", which makes the biases in audience demographics for over 20,000 news outlets on Facebook transparent to any Internet user.

Mapping global variation in human mobility

Abstract: The geographic variation of human movement is largely unknown, mainly due to a lack of accurate and scalable data. Here we describe global human mobility patterns, aggregated from over 300 million smartphone users. The data cover nearly all countries and 65% of Earth’s populated surface, including cross-border movements and international migration. This scale and coverage enable us to develop a globally comprehensive human movement typology. We quantify how human movement patterns vary across sociodemographic and environmental contexts and present international movement patterns across national borders. Fitting statistical models, we validate our data and find that human movement laws apply at 10 times shorter distances and movement declines 40% more rapidly in low-income settings. These results and data are made available to further understanding of the role of human movement in response to rapid demographic, economic and environmental changes. Using large-scale data, Kraemer et al. find that human mobility patterns vary across the globe and in scale by environmental and sociodemographic contexts. There are tenfold differences in mobility patterns depending on the countries’ economic development.

Gender gaps in urban mobility

Abstract: Mobile phone data have been extensively used to study urban mobility. However, studies based on gender-disaggregated large-scale data are still lacking, limiting our understanding of gendered aspects of urban mobility and our ability to design policies for gender equality. Here we study urban mobility from a gendered perspective, combining commercial and open datasets for the city of Santiago, Chile. We analyze call detail records for a large cohort of anonymized mobile phone users and reveal a gender gap in mobility: women visit fewer unique locations than men, and distribute their time less equally among such locations. Mapping this mobility gap over administrative divisions, we observe that a wider gap is associated with lower income and lack of public and private transportation options. Our results uncover a complex interplay between gendered mobility patterns, socio-economic factors and urban affordances, calling for further research and providing insights for policymakers and urban planners.

On Microtargeting Socially Divisive Ads: A Case Study of Russia-Linked Ad Campaigns on Facebook

Abstract: Targeted advertising is meant to improve the efficiency of matching advertisers to their customers However, targeted advertising can also be abused by malicious advertisers to efficiently reach people susceptible to false stories, stoke grievances, and incite social conflict Since targeted ads are not seen by non-targeted and non-vulnerable people, malicious ads are likely to go unreported and their effects undetected This work examines a specific case of malicious advertising, exploring the extent to which political ads1 from the Russian Intelligence Research Agency (IRA) run prior to 2016 US elections exploited Facebook's targeted advertising infrastructure to efficiently target ads on divisive or polarizing topics (eg, immigration, race-based policing) at vulnerable sub-populations In particular, we do the following: (a) We conduct US census-representative surveys to characterize how users with different political ideologies report, approve, and perceive truth in the content of the IRA ads Our surveys show that many ads are "divisive": they elicit very different reactions from people belonging to different socially salient groups (b) We characterize how these divisive ads are targeted to sub-populations that feel particularly aggrieved by the status quo Our findings support existing calls for greater transparency of content and targeting of political ads (c) We particularly focus on how the Facebook ad API facilitates such targeting We show how the enormous amount of personal data Facebook aggregates about users and makes available to advertisers enables such malicious targeting