Mobile Value Added Services: A Business Growth Opportunity for Women Entrepreneurs

Examines the potential for mobile value-added services adoption by women entrepreneurs in Egypt, Nigeria, and Indonesia in expanding their micro businesses; challenges, such as access to digital channels; and the need for services tailored to women

Mapping urban socioeconomic inequalities in developing countries through Facebook advertising data

Ending poverty in all its forms everywhere is the number one Sustainable Development Goal of the UN 2030 Agenda. To monitor the progress toward such an ambitious target, reliable, up-to-date and fine-grained measurements of socioeconomic indicators are necessary. When it comes to socioeconomic development, novel digital traces can provide a complementary data source to overcome the limits of traditional data collection methods, which are often not regularly updated and lack adequate spatial resolution. In this study, we collect publicly available and anonymous advertising audience estimates from Facebook to predict socioeconomic conditions of urban residents, at a fine spatial granularity, in four large urban areas: Atlanta (USA), Bogotá (Colombia), Santiago (Chile), and Casablanca (Morocco). We find that behavioral attributes inferred from the Facebook marketing platform can accurately map the socioeconomic status of residential areas within cities, and that predictive performance is comparable in both high and low-resource settings. Our work provides additional evidence of the value of social advertising media data to measure human development and it also shows the limitations in generalizing the use of these data to make predictions across countries

Using Facebook advertising data to describe the socio-economic situation of Syrian refugees in Lebanon

While the fighting in the Syrian civil war has mostly stopped, an estimated 5.6 million Syrians remain living in neighboring countries1. Of these, an estimated 1.5 million are sheltering in Lebanon. Ongoing efforts by organizations such as UNHCR to support the refugee population are often ineffective in reaching those most in need. According to UNHCR's 2019 Vulnerability Assessment of Syrian Refugees Report (VASyR), only 44% of the Syrian refugee families eligible for multipurpose cash assistance were provided with help, as the others were not captured in the data. In this project, we are investigating the use of non-traditional data, derived from Facebook advertising data, for population level vulnerability assessment. In a nutshell, Facebook provides advertisers with an estimate of how many of its users match certain targeting criteria, e.g., how many Facebook users currently living in Beirut are “living abroad,” aged 18–34, speak Arabic, and primarily use an iOS device. We evaluate the use of such audience estimates to describe the spatial variation in the socioeconomic situation of Syrian refugees across Lebanon. Using data from VASyR as ground truth, we find that iOS device usage explains 90% of the out-of-sample variance in poverty across the Lebanese governorates. However, evaluating predictions at a smaller spatial resolution also indicate limits related to sparsity, as Facebook, for privacy reasons, does not provide audience estimates for fewer than 1,000 users. Furthermore, comparing the population distribution by age and gender of Facebook users with that of the Syrian refugees from VASyR suggests an under-representation of Syrian women on the social media platform. This work adds to growing body of literature demonstrating the value of anonymous and aggregate Facebook advertising data for analysing large-scale humanitarian crises and migration events

Using Facebook advertising data to describe the socio-economic situation of Syrian refugees in Lebanon

While the fighting in the Syrian civil war has mostly stopped, an estimated 5.6 million Syrians remain living in neighboring countries1. Of these, an estimated 1.5 million are sheltering in Lebanon. Ongoing efforts by organizations such as UNHCR to support the refugee population are often ineffective in reaching those most in need. According to UNHCR's 2019 Vulnerability Assessment of Syrian Refugees Report (VASyR), only 44% of the Syrian refugee families eligible for multipurpose cash assistance were provided with help, as the others were not captured in the data. In this project, we are investigating the use of non-traditional data, derived from Facebook advertising data, for population level vulnerability assessment. In a nutshell, Facebook provides advertisers with an estimate of how many of its users match certain targeting criteria, e.g., how many Facebook users currently living in Beirut are “living abroad,” aged 18–34, speak Arabic, and primarily use an iOS device. We evaluate the use of such audience estimates to describe the spatial variation in the socioeconomic situation of Syrian refugees across Lebanon. Using data from VASyR as ground truth, we find that iOS device usage explains 90% of the out-of-sample variance in poverty across the Lebanese governorates. However, evaluating predictions at a smaller spatial resolution also indicate limits related to sparsity, as Facebook, for privacy reasons, does not provide audience estimates for fewer than 1,000 users. Furthermore, comparing the population distribution by age and gender of Facebook users with that of the Syrian refugees from VASyR suggests an under-representation of Syrian women on the social media platform. This work adds to growing body of literature demonstrating the value of anonymous and aggregate Facebook advertising data for analysing large-scale humanitarian crises and migration events

Filtration Failure: On Selection for Societal Sanity

This paper focuses on the question of filtration through the perspective of “too much information”. It concerns Western society within the context of new media and digital culture. The main aim of this paper is to apply a philosophical reading on the video game concept of Selection for Societal Sanity within the problematics of cultural filtration, control of behaviors and desire, and a problematization of trans-individuation that the selected narrative conveys. The idea of Selection for Societal Sanity, which derives from the first postmodern video game Metal Gear Solid 2: Sons of Liberty (2001), is applied into a philosophical framework based on select concepts from Bernard Stiegler’s writing and incorporating them with current events such as post-truth or fake news in order to explore the role of techne and filtration within social organizations and individual psyches. Alternate forms of behavior, which contest cultural paradigms, are re-problematized as tension between calculability and incalculability, or market value versus social bonding

A manifesto for the creative economy

The UK\u27s creative economy is one of its great national strengths, historically deeply rooted and accounting for around one-tenth of the whole economy. It provides jobs for 2.5 million people – more than in financial services, advanced manufacturing or construction – and in recent years, this creative workforce has grown four times faster than the workforce as a whole. But behind this success lies much disruption and business uncertainty, associated with digital technologies. Previously profitable business models have been swept away, young companies from outside the UK have dominated new internet markets, and some UK creative businesses have struggled to compete. UK policymakers too have failed to keep pace with developments in North America and parts of Asia. But it is not too late to refresh tired policies. This manifesto sets out our 10-point plan to bolster one of the UK\u27s fastest growing sectors

The New News: Journalism We Want and Need

Economic pressures on one hand and continuing democratization of news on the other have already changed the news picture in Chicago, as elsewhere in the U.S. The Chicago Tribune and Chicago Sun-Times are in bankruptcy, and local broadcast news programs also face economic pressures. Meanwhile, it seems every week brings a new local news entrepreneur from Gapers Block to Beachwood Reporter to Chi-Town Daily News to Windy Citizen to The Printed Blog.In response to these changes, the Knight Foundation is actively supporting a national effort to explore innovations in how information, especially at the local community level, is collected and disseminated to ensure that people find the information they need to make informed decisions about their community's future. The Chicago Community Trust is fortunate to have been selected as a partner working with the Knight Foundation in this effort through the Knight Community Information Challenge. For 94 years, the Trust has united donors to create charitable resources that respond to the changing needs of our community -- meeting basic needs, enriching lives and encouraging innovative ways to improve our neighborhoods and communities.Understanding how online information and communications are meeting, or not, the needs of the community is crucial to the Trust's project supported by the Knight Foundation. To this end, the Trust commissioned the Community Media Workshop to produce The New News: Journalism We Want and Need. We believe this report is a first of its kind resource offering an inventory and assessment of local news coverage for the region by utilizing the interactive power of the internet. Essays in this report also provide insightful perspectives on the opportunities and challenges

Interpreting wealth distribution via poverty map inference using multimodal data

Poverty maps are essential tools for governments and NGOs to track socioeconomic changes and adequately allocate infrastructure and services in places in need. Sensor and online crowd-sourced data combined with machine learning methods have provided a recent breakthrough in poverty map inference. However, these methods do not capture local wealth fluctuations, and are not optimized to produce accountable results that guarantee accurate predictions to all sub-populations. Here, we propose a pipeline of machine learning models to infer the mean and standard deviation of wealth across multiple geographically clustered populated places, and illustrate their performance in Sierra Leone and Uganda. These models leverage seven independent and freely available feature sources based on satellite images, and metadata collected via online crowd-sourcing and social media. Our models show that combined metadata features are the best predictors of wealth in rural areas, outperforming image-based models, which are the best for predicting the highest wealth quintiles. Our results recover the local mean and variation of wealth, and correctly capture the positive yet non-monotonous correlation between them. We further demonstrate the capabilities and limitations of model transfer across countries and the effects of data recency and other biases. Our methodology provides open tools to build towards more transparent and interpretable models to help governments and NGOs to make informed decisions based on data availability, urbanization level, and poverty thresholds.Comment: 12 pages. In Proceedings of the ACM Web Conference 2023 (WWW'23