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Hacking Utopia

Organizado por: Universidad de Friburgo

Lugar: Suiza

Fecha: Noviembre del 2017

Auspiciantes: Swiss Post, Locatee, ELCA

Dos equipos de Taws Participaron en el Hackathon, siendo los únicos en modalidad remota y de Latinoamérica.

El desafío escogido fue el de Análisis Predictivo, el cual consistía en la predicción del grado de ocupación del edificio sobre la base de datos históricos de Locatee Analytics y otras fuentes de datos como previsiones meteorológicas, vacaciones escolares, etc. Esto permite cerrar pisos separados en ciertos días si ya es previsible que no se utilizarán hasta su capacidad máxima. Por lo tanto, el rendimiento de limpieza y la energía se pueden ahorrar.

El equipo desarrollo un modelo de tal forma que indique a qué hora del día iba a estar un espacio lleno por una determinada cantidad de personas, de ese modo los encargados del edificio iban a saber cómo administrar mejor los recursos de este.

El hackathon duro 3 días, la presentación fue vía Skype y en inglés, al final del evento el equipo fue ganador del segundo lugar.

 

Predicting Venezuelan states political election results through Twitter

Authors: Rodrigo Castro , Leonardo Kuffó , Carmen Vaca

Abstract:

The large adoption of Twitter during electioneering has created an unprecedented opportunity to capture the citizen's behaviour nationwide. The real-time access to information published by citizens has motivated researchers to design methods in order to enrich traditional political polling with insights from this rich source of data. However, less work has been done to capture the political scenario in Latin American countries, given that some methods rely on the use of English words, the reproducibility of such studies in Spanish speaking countries is a challenging task. Therefore, we propose a framework in which we apply social network analysis techniques and unsupervised machine learning to infer the political alignment at state level during Venezuelan Parliamentary election, which were performed on December 6, 2015. This electoral process took place in the middle of an acute political polarization in the country, the masses were organized around two political coalitions with opposite ideology: Government and opposition. In order to discover automatically the corresponding state political preferences, we analyze 60K tweets posted within the Venezuelan geographic boundaries during one week before the election day. Applying our framework, we are able to infer a given state political alignment starting from the quantified differences in communication patterns and linguistic profiles of the state aggregated tweets. We demonstrate that the online political atmosphere reflects the offline tendency at state scale given that we are able to predict the election tendency in Venezuela states with an accuracy of 87.5% with respect to official election results publicly available.

 

Secrets of Quito: Discovering a city through TripAdvisor

Authors: Madelyne Velasco ; César San Lucas ; Kevin Ortiz ; José Vélez ; Carmen Vaca

Abstract:

People generate online content everyday at every hour at social networks. Social networks are a medium in which people can give their opinion on different topics and obtain new information. The content people create can be useful for researchers to understand human behavior in cities such as Quito. In this work, we are going to describe how Quito city is described on the travel network TripAdvisor by users based on geo referenced and lexical information. To describe this, we are going to combine a clustering algorithm(k-means) and a numerical statistic for information retrieval (TF-IDF). The results are later compared to information the municipality obtained from field studies to determine if they have similar behaviors.

 

Requiem for online harassers: Identifying racism from political tweets

Authors: Estefanía Lozano ; Jorge Cedeño ; Galo Castillo ; Fabricio Layedra ; Henry Lasso ; Carmen Vaca

Abstract:

During the last five years, the amount of users of online social networks has increased exponentially. With the growing of users, social problems also arise. Due to the nature of these platforms, specifically Twitter, users can express their ideas in the way they prefer no matter if it is racist or not. As the Twitter CEO says, one of the most difficult things for them is to detect and ban people who harass others. Researches have addressed this issue in recent years. However, it is needed a wider range of strategies to detect racist users and content. In this work, we collect tweets produced by the ego networks of the two former 2016 US Presidential Candidates: Hillary Clinton and Donald Trump, grouped in four datasets. After deleting spammers, we get 84,371 unique users labeled by using two different metrics: Sentiment Word Count and Racist Score. Both of them let us not only to identify users as racists, but also to detect the level of negativism by analyzing their most recent 200 tweets, increasing the effectiveness of the method. Using it, we find the most negative and racist user and the most positive and non-racist user from all datasets. Taking advantage of the topological properties of the ego networks we analyzed, we also verify that our results satisfy the sociologist theory of homophily; where the followers of each candidate represent their homophilous. For a nation as the United States of America, detecting online harassers might help to decrease racism and cyberbullying, social problems that affect their society. A world without online harassers is an utopia, but this is one step to achieve it.