Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userKillings of social leaders in the Colombian post-conflict: Data analysis for investigative journalism
96
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
After the peace agreement of 2016 with FARC, the killings of social leaders have emerged as an important post-conflict challenge for Colombia. We present a data analysis based on official records obtained from the Colombian General Attorneys Office spanning the time period from 2012 to 2017. The results of the analysis show a drastic increase in the officially recorded number of killings of democratically elected leaders of community organizations, in particular those belonging to Juntas de Accion Comunal [Community Action Boards]. These are important entities that have been part of the Colombian democratic apparatus since 1958, and enable communities to advocate for their needs. We also describe how the data analysis guided a journalistic investigation that was motivated by the Colombian governments denial of the systematic nature of social leaders killings.
rate research
Read More
The following paper explores the possibility of using Machine Learning algorithms to detect the cases of corruption and malpractice by governments. The dataset used by the authors contains information about several government contracts in Colombia from year 2007 to 2012. The authors begin with exploring and cleaning the data, followed by which they perform feature engineering before finally implementing Machine Learning models to detect anomalies in the given dataset.
The operation of adding edges has been frequently used to the study of opinion dynamics in social networks for various purposes. In this paper, we consider the edge addition problem for the DeGroot model of opinion dynamics in a social network with $n$ nodes and $m$ edges, in the presence of a small number $s ll n$ of competing leaders with binary opposing opinions 0 or 1. Concretely, we pose and investigate the problem of maximizing the equilibrium overall opinion by creating $k$ new edges in a candidate edge set, where each edge is incident to a 1-valued leader and a follower node. We show that the objective function is monotone and submodular. We then propose a simple greedy algorithm with an approximation factor $(1-frac{1}{e})$ that approximately solves the problem in $O(n^3)$ time. Moreover, we provide a fast algorithm with a $(1-frac{1}{e}-epsilon)$ approximation ratio and $tilde{O}(mkepsilon^{-2})$ time complexity for any $epsilon>0$, where $tilde{O}(cdot)$ notation suppresses the ${rm poly} (log n)$ factors. Extensive experiments demonstrate that our second approximate algorithm is efficient and effective, which scales to large networks with more than a million nodes.
Multimedia content in social media platforms provides significant information during disaster events. The types of information shared include reports of injured or deceased people, infrastructure damage, and missing or found people, among others. Although many studies have shown the usefulness of both text and image content for disaster response purposes, the research has been mostly focused on analyzing only the text modality in the past. In this paper, we propose to use both text and image modalities of social media data to learn a joint representation using state-of-the-art deep learning techniques. Specifically, we utilize convolutional neural networks to define a multimodal deep learning architecture with a modality-agnostic shared representation. Extensive experiments on real-world disaster datasets show that the proposed multimodal architecture yields better performance than models trained using a single modality (e.g., either text or image).
Development of sustainable insurance for cyber risks, with associated benefits, inter alia requires reduction of ambiguity of the risk. Considering cyber risk, and data breaches in particular, as a man-made catastrophe clarifies the actuarial need for multiple levels of analysis - going beyond claims-driven loss statistics alone to include exposure, hazard, breach size, and so on - and necessitating specific advances in scope, quality, and standards of both data and models. The prominent human element, as well as dynamic, networked, and multi-type nature, of cyber risk makes it perhaps uniquely challenging. Complementary top-down statistical, and bottom-up analytical approaches are discussed. Focusing on data breach severity, measured in private information items (ids) extracted, we exploit relatively mature open data for U.S. data breaches. We show that this extremely heavy-tailed risk is worsening for external attacker (hack) events - both in frequency and severity. Writing in Q2-2018, the median predicted number of ids breached in the U.S. due to hacking, for the last 6 months of 2018, is 0.5 billion. But with a 5% chance that the figure exceeds 7 billion - doubling the historical total. Fortunately the total breach in that period turned out to be near the median.
The Ubiquitous nature of smartphones has significantly increased the use of social media platforms, such as Facebook, Twitter, TikTok, and LinkedIn, etc., among the public, government, and businesses. Facebook generated ~70 billion USD in 2019 in advertisement revenues alone, a ~27% increase from the previous year. Social media has also played a strong role in outbreaks of social protests responsible for political changes in different countries. As we can see from the above examples, social media plays a big role in business intelligence and international politics. In this paper, we present and discuss a high-level functional intelligence model (recipe) of Social Media Analysis (SMA). This model synthesizes the input data and uses operational intelligence to provide actionable recommendations. In addition, it also matches the synthesized function of the experiences and learning gained from the environment. The SMA model presented is independent of the application domain, and can be applied to different domains, such as Education, Healthcare and Government, etc. Finally, we also present some of the challenges faced by SMA and how the SMA model presented in this paper solves them.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
