Throughout history, social movements have often been catalysts for radical societal change. In the past two decades, hashtag activism, the use of social media platforms for internet activism, has become a driving force behind the development of social movements across the world. From #MeToo to #IdleNoMore and most recently #JusticeForGeorgeFloyd, social media has been used strategically by activists to mobilize communities to come together and protest against different forms of injustice. In the field of social movements science, a large body of research has studied the role of hashtag activism for the formation of social movements, but less efforts have been allocated towards the study of the spatio-temporal relationship that exists between hashtag activism and political processes. In other words, in spite of the a-spatial nature of social media, can the study of hashtag activism help us understand human behaviours and societal processes that occur off-line, in the physical space? Such conundrum is the basis of the research in this master thesis, where a data-driven framework is implemented to investigate the spatio-temporal relationship between hashtag activism and two important political processes: physical protest activity and legislative action. Through a combination of time series analysis, regression, geo-spatial analysis, and machine learning, and the use of a large Twitter data-set of geo-located social media posts, such relationships are quantified, modelled and visualized for the 2020 #JusticeForGeorgeFloyd social movement. First, time series analysis is used to measure the temporal relationship between hashtag activism, physical protest, and legislative action. Second, geo-spatial analysis is used to compare the nature of this relationship across different spatial resolutions. Finally, regression analysis and deep learning are used to model, generalize and forecast such spatio-temporal relationships. The outcomes of this research are two-fold. First, in the case of #JusticeForGeorgeFloyd, it was found that at the national, state, and county scale, hashtag activism bears a very strong positive temporal relationship with physical protest activity. Through the statistical modelling of this relationship, it is possible to predict the intensity of national, state, and county level protest activity on a given day based on the intensity of state and county level hashtag activism the day before. At the county level, it was found that in some "outlier" counties the temporal relationship between hashtag activism and physical protest is characterized by a disproportional amount of physical protest compared to the intensity of hashtag activism, while in other counties this relationship is characterized by a disproportional amount of hashtag activism compared to the intensity of physical protests. In order to make sense of this tendency this research proposes the Mobilization Synergy Index (MSI), which makes it possible to statistically quantify this trade-off, and to visualize its spatial distribution in an intuitive way. Through this index, both local policy makers and activists could gain valuable insight into their communities and as a result adopt strategies that better reflect community needs and concerns. Second, the analysis of instances of hashtag activism in relation to legislative responses revealed that states in which people engaged more in hashtag activism on average during #JusticeForGeorgeFloyd also experienced more legislative responses related to policing. Additionally, results from temporally disaggregated national level and state level data suggests that the number of legislative responses on a given day bears a very similar positive relationship with each of hashtag activism activity and physical protest activity taken at a specific time lag prior to the day in which the legislative responses occurred. At the state level, it was found that while both hashtag activism and physical protest activity result in future legislative responses, this temporal relationship is highly variable by state. Such tendency may serve as an indicator of state-level political responsiveness during times of crisis. To the best of our knowledge, this is the first national level quantitative study aimed at measuring the temporal relationships between hashtag activism, physical protest activity, and legislative action, at various spatial resolutions. Therefore, the study provides several contributions to the field of social movements research and, more broadly, the computational social sciences.

Disparity in spatial accessibility is strongly associated with growing inequalities among urban communities. Since improving levels of accessibility for certain communities can provide them with upward social mobility and address social exclusion and inequalities in cities, it is important to understand the nature and distribution of spatial accessibility among urban communities. To support decision-makers in achieving inclusion and fairness in policy interventions in cities, we present an open and data-driven framework to understand the spatial nature of accessibility to infrastructure among the different demographics. We find that accessibility to a wide range of infrastructure in any city (54 cities) converges to a Zipf’s law, suggesting that inequalities also appear proportional to growth processes in these cities. Then, assessing spatial inequalities among the socioeconomically clustered urban profiles for 10 of those cities, we find urban communities are distinctly segregated along social and spatial lines. We find low accessibility scores for populations who have a larger share of minorities, earn less and have a relatively lower number of individuals with a university degree. These findings suggest that the reproducible framework we propose may be instrumental in understanding processes leading to spatial inequalities and in supporting cities to devise targeted measures for addressing inequalities for certain underprivileged communities.@en