Tahar Zanouda

Trump vs. Hillary: What Went Viral During the 2016 US Presidential Election

In this paper, we present quantitative and qualitative analysis of the top retweeted tweets (viral tweets) pertaining to the US presidential elections from September 1, 2016 to Election Day on November 8, 2016. For everyday, we tagged the top 50 most retweeted tweets as supporting or attacking either candidate or as neutral/irrelevant. Then we analyzed the tweets in each class for: general trends and statistics; the most frequently used hashtags, terms, and locations; the most retweeted accounts and tweets; and the most shared news and links. In all we analyzed the 3,450 most viral tweets that grabbed the most attention during the US election and were retweeted in total 26.3 million times accounting over 40% of the total tweet volume pertaining to the US election in the aforementioned period. Our analysis of the tweets highlights some of the differences between the social media strategies of both candidates, the penetration of their messages, and the potential effect of attacks on both.

A multiplex approach to urban mobility

Multilayer networks have been the subject of intense research in the recent years in different applications. However, in urban mobility, the multi-layer nature of transportation systems has been generally ignored, even though most large cities are spanned by more than one transportation system. These different modes of transport have usually been studied separately. It is however important to understand the interplay between different transport modes. In this study, we consider the multimodal transportation system, represented as a multiplex network, and we address the problem of urban mobility in the transportation system, in addition to its robustness and resilience under random and targeted failures. Multiplex networks are formed by a set of nodes connected by links having different relationships forming the different layers of the multiplex. We study, in particular, how random and targeted failures to the transportation multiplex network affect the way people travel in the city. More specifically, we are interested in assessing the portion of the city covered by a random walker under various scenarios. We consider the public transport of London as an application to illustrate the proposed capacity analysis method of multi-modal transportation, and we report on the robustness and the resilience of the system. This study is part of a project to develop a computational framework to better understand and predict mobility patterns in the city of Doha once its ambitious metro system is deployed in 2019. The computational framework will help the city to efficiently manage the flow of people and intelligently handle capacity through different transportation modes, in particular during mega events such as Soccer Wold cup FIFA 2022. The proposed method is based on the study in [9], but with an efficient computational approach resulting in tremendous savings in computational time. It is scalable and lends itself to efficient implementation on parallel computers.

Resilience analytics: coverage and robustness in multi-modal transportation networks

A multi-modal transportation system of a city can be modeled as a multiplex network with different layers corresponding to different transportation modes. These layers include, but are not limited to, bus network, metro network, and road network. Formally, a multiplex network is a multilayer graph in which the same set of nodes are connected by different types of relationships. Intra-layer relationships denote the road segments connecting stations of the same transportation mode, whereas inter-layer relationships represent connections between different transportation modes within the same station. Given a multi-modal transportation system of a city, we are interested in assessing its quality or efficiency by estimating the coverage i.e., a portion of the city that can be covered by a random walker who navigates through it within a given time budget, or steps. We are also interested in the robustness of the whole transportation system which denotes the degree to which the system is able to withstand a random or targeted failure affecting one or more parts of it. Previous approaches proposed a mathematical framework to numerically compute the coverage in multiplex networks. However solutions are usually based on eigenvalue decomposition, known to be time consuming and hard to obtain in the case of large systems. In this work, we propose MUME, an efficient algorithm for Multi-modal Urban Mobility Estimation, that takes advantage of the special structure of the supra-Laplacian matrix of the transportation multiplex, to compute the coverage of the system. We conduct a comprehensive series of experiments to demonstrate the effectiveness and efficiency of MUME on both synthetic and real transportation networks of various cities such as Paris, London, New York and Chicago. A future goal is to use this experience to make projections for a fast growing city like Doha.

Structural robustness and service reachability in urban settings

The concept of city or urban resilience has emerged as one of the key challenges for the next decades. As a consequence, institutions like the United Nations or Rockefeller Foundation have embraced initiatives that increase or improve it. These efforts translate into funded programs both for action “on the ground” and to develop quantification of resilience, under the for of an index. Ironically, on the academic side there is no clear consensus regarding how resilience should be quantified, or what it exactly refers to in the urban context. Here we attempt to link both extremes providing an example of how to exploit large, publicly available, worldwide urban datasets, to produce objective insight into one of the possible dimensions of urban resilience. We do so via well-established methods in complexity science, such as percolation theory—which has a long tradition at providing valuable information on the vulnerability in complex systems. Our findings uncover large differences among studied cities, both regarding their infrastructural fragility and the imbalances in the distribution of critical services.

On the role of political affiliation in human perception the case of Delhi OddEven experiment

In an effort to curb air pollution, the city of Delhi (India), known to be one of the most populated, polluted, and congested cities in the world has run a trial experiment in two phases of 15 days intervals. During the experiment, most of four-wheeled vehicles were constrained to move on alternate days based on whether their plate numbers ended with odd or even digits. While the local government of Delhi represented by A. Kejriwal (leader of AAP party) advocated for the benefits of the experiment, the prime minister of India, N. Modi (former leader of BJP) defended the inefficiency of the initiative. This later has led to a strong polarization of public opinion towards OddEven experiment. This real-world urban experiment provided the scientific community with a unique opportunity to study the impact of political leaning on humans perception at a large-scale. We collect data about pollution and traffic congestion to measure the real effectiveness of the experiment. We use Twitter to capture the public discourse about the experiment in order to study people’s opinion within different dimensions: time, location, and topics. Our results reveal a strong influence of political affiliation on how people perceived the outcomes of the experiment. For instance, AAP supporters were significantly more enthusiastic about the success of OddEven compared to BJP supporters. However, taking into account location of people revealed that personal experience is able to overcome political bias.