class: center, middle, inverse, title-slide .title[ # Measuring Gender Divides with Facebook Data ] .author[ ### David Garcia <br> <em>ETH Zurich</em> ] .date[ ### Social Data Science ] --- layout: true <div class="my-footer"><span>David Garcia - Social Data Science</span></div> --- class:center # How can social media affect our society? .pull-left[ Opportunities and benefits <img src="figures/FBmap.png" width="950" /> ] -- .pull-right[ Risks and problems <img src="figures/zuck.png" width="950" /> ] --- layout: true <div class="my-footer"><span> <a href=http://www.pnas.org/content/early/2018/06/12/1717781115> Analyzing gender inequality through large-scale Facebook advertising data. David Garcia et al. Proceedings of the National Academy of Sciences (2018)</a></span></div> --- # Inequality and gender in social media - The Internet as the Great Potential Equalizer (Hargittai & Hsieh 2013): - Providing opportunities and information access to everyone? - "Unequal distribution of Internet access might exacerbate socioeconomic inequalities." <div style="text-align: right"> <em property="italic"> World Bank: World Development Report 2016: Digital Dividends </em></div> - "One in five female Internet users live in countries where harassment and abuse of women online is extremely unlikely to be punished." <div style="text-align: right"> <em property="italic"> The Web and Rising Global Inequality. World Wide Web Foundation, 2015 </em></div> --- ## The Facebook Ads API in demographics <img src="figures/FBAPI.png" width="800" style="display: block; margin: auto;" /> - Programmatic Interface to automate marketing campaign in Facebook - Audiences can be tuned by demographic attributes (gender, age, income...) or interests (sports, political parties, entertainment...) - Accessible for everyone with a Facebook account - audience metrics for everyone - Used in demographics and development research (e.g. [Zagheni, Weber, & Gummadi, 2017;](https://doi.org/10.1111/padr.12102) [Mejova et al., 2018;](https://dl.acm.org/doi/abs/10.1145/3209811.3212698) [Fatehkia, Kashyap, & Weber, 2018](https://www.sciencedirect.com/science/article/pii/S0305750X18300883)) - Python package to use it: https://github.com/maraujo/pySocialWatcher --- ## The Facebook Gender Divide `\(R_{g,c}\)`: Ratio of Facebook active users of gender *g* over all inhabitants of gender *g* in country *c* <center> Facebook Gender Divide for country c: <br>$$FGD_c= log\left(\frac{R_{Male,c}}{R_{Female_c}}\right)$$</center> Validation with survey data: - FGD (217 countries) - Global Web Index (34 countries) - Pew Internet research (19 countries) --- # Validation against international surveys .pull-left[ <img src="figures/FGDval1.png" width="500" style="display: block; margin: auto;" /> ] .pull-right[ <img src="figures/FGDval2.png" width="550" style="display: block; margin: auto;" /> ] --- ## The Facebook Gender Divide across countries <img src="figures/FGDmap.jpg" width="1100" style="display: block; margin: auto;" /> --- # Basics: Linear Regression Regression models formalize an equation in which one numeric variable `\(Y\)` is formulated as a linear function of other variables `\(X_1\)`, `\(X_2\)`, `\(X_3\)`, etc: <center> `\(Y = a + b_1 X_1 + b_2 X_2 + b_3 X_3 ... + \epsilon\)` </center> - `\(Y\)` is called the dependent variable - `\(X_1\)`, `\(X_2\)`, `\(X_3\)`, etc are called independent variables - `\(a\)` is the intercept, which measures the expected value of `\(Y\)` that does not depend on the dependent variables - `\(b_1\)`, `\(b_2\)`, `\(b_3\)`, etc are called the slopes or the coefficients - `\(\epsilon\)` are the residuals, the errors of the equation in the data --- ## The FGD and offline gender inequality <img src="figures/FGDreg.jpg" width="1100" style="display: block; margin: auto;" /> - Regression of FGD rank on WEF gender indices and controls - Most explanatory variable: Education Gender Equality --- ## The FGD and changes in gender inequality .pull-left[ <img src="figures/FGDdelta.png" width="550" style="display: block; margin: auto;" /> ] .pull-right[ - Changes in FGD and WEF gender equality indicators (2015-2016) - FGD in 2015 improves prediction of changes in economic gender equality - The reverse case is not present - Not observable for other equality indices On average, countries with high FGD rank in 2015 (low FGD) tended to approach economic gender equality in 2016 ] --- ## Positive externalities in Facebook use .pull-left[ <img src="figures/Externalities.jpg" width="450" style="display: block; margin: auto;" /> ] .pull-right[ <img src="figures/Scaling.png" width="550" style="display: block; margin: auto;" /> ] - Superlinear scaling of activity ratio versus total penetration - Positive externality: value of using Facebook grows with other people using it --- ## The Facebook Gender Divide: Summary - Measure of gender divide with FB data comparable to surveys - This measure captures offline gender equality indices across countries - Temporal correlation between FGD and changes in economic gender eq. - Superlinear scaling of FB activity with penetration: positive externality Social media might also work as a *digital provide*. Is Facebook helping women to find employment in countries with high gender inequality? --- # To learn more <img src="figures/PNAS.png" width="1100" style="display: block; margin: auto;" /> [**Analyzing gender inequality through large-scale Facebook advertising data.** David Garcia et al. Proceedings of the National Academy of Sciences (2018)](http://www.pnas.org/content/early/2018/06/12/1717781115)