Omdena Chapter Page: El Salvador

Omdena El Salvador Chapter - Omdena Chapters

Upcoming Projects 

We will be running an AI project soon…. Stay Tuned!

Politics Fake News Detector in LATAM (Latin America)

The Background

Since the Cambridge Analytica scandal a pandora box has been opened around the world, bringing to light campaigns even involving our current Latinamerica leaders manipulating public opinion through social media to win an election. There is a common and simple pattern that includes platforms such as facebook and fake news, where the candidates are able to build a nefarious narrative for their own benefit. This fact is a growing concern for our democracies, as many of these practices have been widely spread across the region and more people are gaining access to the internet. Thus, it is a necessity to be able to advise the population, and for that we have to be able to quickly spot these plots on the net before the damage is irreversible.


The Problem


Once the capacity to somewhat detect irregularities in the news activity on the internet is developed, we might be able to counter the disinformation with the help of additional research. As we reduce the time spent in looking for those occurrences, more time can be used in validating the results and uncovering the truth; enabling researchers, journalists and organizations to help people make an informed decision whether the public opinion is true or not, so that they can identify on their own if someone is trying to manipulate them for a certain political benefit.

If  this matter isn’t tackled with enough urgency, we might see the rise of a new dark era in latin america politics, where many unscrupulous parties and people will manage to gain power and control the lives of many people. Therefore, the results of the project can provide support for both private and public companies on their future analysis and activities. Additionally, researchers and students could use the outcomes for their own research or use it for learning purposes.


Una vez contemos con la capacidad de detectar irregularidades en las noticias por internet, seremos capaces de contrarrestar la desinformación con la ayuda de investigaciones adicionales. Mientras reducimos el tiempo invertido en identificar estos patrones, podemos dedicar más tiempo a validar los resultados y buscar las verdades ocultas; habilitando a los investigadores, periodistas y organizaciones a que  ayuden a la población a tomar decisiones informados de la veracidad de la opinión pública, y que estos puedan identificar si alguien está tratando de manipularlos para beneficio político.

Si este problema no se trata con urgencia, podríamos ver el resurgir de una era oscura en la política latina, donde muchos partidos y personajes inescrupulosos tomarán el poder y el control de la vida de las personas. Por lo tanto, los resultados del proyecto pueden ser de provecho para los análisis y actividades futuros de entidades tanto públicas como privadas. Además de que tanto estudiantes como investigadores pueden hacer uso de los entregables para sus propias investigaciones y/o aprendizaje.


The Project Goals

To gather and clean datasets from different newspapers and new outlets in LATAM.

– To predict if there is a political affiliation in a certain topic on the news.

– To compare and determine if there is any irregularity between the information available respecting an specific news in the different news sources

– To understand and visualize the information patterns from the news by creating a visualization dashboard.


The Learning Outcomes

  • 1. How to gather and clean text datasets from news for data modeling.
  • 2. How to use data visualization tools for further app creation and data reporting.
  • 3. How to create a classification model with NLP libraries.


The Tasks & Timeline


Week 1 Week 2 Week 3 Week 4


– Data collection


– Data collection

– Data cleaning





– Topics Analysis

– Data cleaning



– Unsupervised Model Creation

Week 5 Week 6 Week 7 Week 8

Division by Branches


– Political Party Classifier (If feasible)

– Map Visualization



 – Streamlit App


– Streamlit App

– Deployment




Proposals of workshops topics for the challenge:

Select the workshops you would like us to organize for your project from listed down below and share your thoughts and needs during the kick-off meeting. If you would like us to organize a workshop on a topic not listed here, please mention it here and we will try to find a speaker for it. 

  • Data gathering and cleaning for News/text
  • Topic modeling NLP
  • Streamlit – creating interactive visualizations



Completed Projects 

Analyzing Gender Violence on Twitter During COVID19 in El Salvador

The Background 

Social Media has become a main driver for many forms of hate expressions, and gender violence isn’t an exception. Based on the 2020 first semester report of the UNDP, El Salvador registered an increase of 5.2% of violent actions against women only in 2019.

The Problem

There isn’t much available data regardings the problem, and the organizations devoted to actual research don’t have enough resources to actually gather the information themselves. Having access to an updated dataset would leverage the capabilities of organizations to develop solutions that tackle the problem and could be a foundation to further research.

No hay suficiente data respecto del problema, y muchas organizaciones dedicadas a la investigación no cuentan con los recursos para recolectarla por ellas mismas.Tener acceso a un dataset actualizado puede mejorar las capacidades de las organizaciones para desarrollar soluciones al problema y puede ser un punto de partida para otras investigaciones.

The Project Goals

– Identify the main gender violence expressions shared on Twitter in El Salvador during 2020

– Define the categories of violence to be used by the classification model

–  Train a classification that labels violence expressions shared on Twitter based on the previously defined categories

–  Prepare a dataset that contains the output of the model and the most relevant information regarding each of the posts used for inference

The Learning Outcomes

1. How to clean from Twitter and do NLP on social media posts

2. Techniques to define different types of violent expressions used on social networks

3. Train NLP models to do text analysis

4. Prepare and upload a dataset for public availability


The Tasks and Timeline

Week 1 Week 2 Week 3 Week 4

-Data cleaning and preprocessing 

-Additional Twitter scraping

–Exploratory Data Analysis(EDA)

-Categories definition

-Feature Engineering

-Model selection and first experiments


-NLP Model Development

-Documenting the categories


-Prepare the dataset to deliver

-Write the final report

-Share the data with the public

Chapter’s Partner
El Salvador Chapter Lead
Giancarlo Pablo

Giancarlo Pablo

Data Scientist

Giancarlo is a Data Scientist and a Business Management student, who loves working on communities’ buildup and that wants to propel the opportunities to learn and apply AI on El Salvador. He does have experience working with digital products and appreciate the great potential of the many possible implementations to improve the life of his country by leveraging ML solutions while increasing the awareness about potential benefits and risks of AI.