AI, automation, chatbots, machine learning

Listen to these 3 people share how AI is reshaping human’ behavior

You might think that Terminator shows where we are today in terms of the level of AI. Although the explosive growth in information science and machine learning has led to an AI boom, we’re still very far from what the movies show.

Before listening to the below videos talking about how AI is helping humans, let’s try to understand what this is. There are many Artificial Intelligence definitions out there, but I’ll take the below one for a basic introduction:

AI is the field of study of intelligent agents: any system that perceives its environment and takes actions that maximize its chance of achieving its goals.

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automation, chatbots, data, journalism, machine learning, social media, social networks

5 Chatbots that combat fake news on the COVID19 (Updated)

Since the Covid19 arrived in Europe and the alarm state was officially declared in several countries, there have been several initiatives to combat the fake news and avoid a feeling of panic.

First infographics on how the virus was spread out and tips to not get infected were released in newspapers, blogs and government websites raising awareness and claiming a civic duty.

Numbers were increasing as well as fake news on how to prevent the infection or the syntomps. Below, I’ve gathered 5 good examples of how official institutions have used messaging apps such as WhatsApp to combat fake news and bring information closer to citizens:

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data journalism, data visualization, infographic, Interactivity, journalism

Visualising the #COVID19 (II): 5 good examples

On this post last week, I gathered 6 good examples of how journalists and researchers have been visualising the #COVID19 and providing different angles of the story.

Below I’m providing 5 more examples (some shared as feedback after the first post):

1. How a virus became a pandemic (New York Times)

The New York Times showcases through a long story format how the virus started on the  ‘worst’ timing ever, when hundreds of millions of people were traveling back to their hometowns for the Lunar New Year from Wuhan.

It is said that about 7 million people left in January, before the travel was restricted. And when China limited local travels, international trips still continued as normal.

2. Who is facing the greatest coronavirus risk? 

Lazaro Gamio visualises on the New York Times all workers (from the Department of Labor database) and the risk level of these with the #COVID19.

This database gathers aspects such as the number of times that a telephone is used or how often one needs to bend their body.

The Y axis measures how often workers are exposed to the disease and the X axis the physical proximity to others.

You can look for a particular job or explore the different bubbles: (1)

3. Are the U.S. hospitals ready for Coronavirus? 

ProPublica presents nine different scenarios where the U.S. health care system is (or is not) able to cope with multiple people infected from the Coronavirus.

At first glance, you can review if your region will be collapsed with the situation or not while the percentage of infected people increase.

Do you want to know more? You can look for a specific hospital nearby you:


4. How can a passenger get infected?

This article on the South Morning Post explains that one person can infect others even if they are more than 4 m away.

Taking a bus as an example, the graphic represents the infected passenger, other people using a mask or not, and how easily can these be infected being 4.5 metres away.


5. The global spread over time

Tagesspiegel publishes a long format to visualise the spread of the virus. On the animated map, they highlight in yellow countries that were reporting first cases while the cases of people infected were rapidly increasing:



Any more examples? Let me know in the comments or at @mcrosasb

data, data journalism, data visualization, infographic, journalism, maps

Visualising pandemics between 1660-1920: 10 good examples

Data journalism is playing a role during the Coronavirus outbreak on informing users who, from their homes or jobs, are following its fast spread. How were journalists and scientific using charts, maps or diagrams to represent this information back in 1660? Below I’ve gathered 10 examples of four different epidemics.

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data journalism

Visualising the #COVID19: 6 good examples

#Covid_19 and #coronapocalypse have been trending hashtags for the last few weeks, being  used in 1436K tweets in less than 24 hours.

In addition to these two hashtags, #StaytheFuckHome has also gained attraction on social media. This movement aims to raise awareness for citizens and help to stop the COVID-19 pandemic by providing a self-quarantine manifesto and encouraging a social media proactive attitude.

In a world where some citizens rely more on Twitter or other social media channels to get accurate and updated news, it’s more important than never to take some time when looking for this information and review it carefully.

Below I’ve gathered 6 good examples (and discussions) that might give you another angle of the Coronavirus and prevent yourself from disinformation.

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data journalism, data visualization, social media, social networks, twitter

10 takeaways from the 2020 USA Marathon event

If you only had 5 minutes to pitch about the US Presidential elections, what would you say?

On Tuesday, Ideograma invited 20 professionals on political communication to share main ideas on a 100 minutes marathon. From guessing who will be the winning candidate, the current use of social media, women candidates racing to the White house to a short bio of each politician, all speakers shared different insights from these upcoming elections.


Below 10 takeaways that you might find useful if you have little knowledge on next year’s elections:

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