Tag: computational science

Understanding Polarized Political Events through Social Media Analysis

by Marco Brambilla, posted in data science, social network, social network analysis

Predicting the outcome of elections is a topic that has been extensively studied in political polls, which have generally provided reliable predictions by means of statistical models. In recent years, online social media platforms have become a potential alternative to traditional polls, since they provide large amounts of post and user data, also referring to socio-political aspects.

In this context, we designed a research that aimed at defining a user modeling pipeline to analyze dis cussions and opinions shared on social media regarding polarized political events (such as a public poll or referendum).

The pipeline follows a four-step methodology.

 

  • First, social media posts and users metadata are crawled.
  • Second, a filtering mechanism is applied to filter out spammers and bot users.
  • Third, demographics information is extracted out of the valid users, namely gender, age, ethnicity and location information.
  • Fourth, the political polarity of the users with respect to the analyzed event is predicted.

In the scope of this work, our proposed pipeline is applied to two referendum scenarios:

  • independence of Catalonia in Spain
  • autonomy of Lombardy in Italy

We used these real-world examples to assess the performance of the approach with respect to the capability of collecting correct insights on the demographics of social media users and of predicting the poll results based on the opinions shared by the users.

Cursor_and_KDWEB_2018_paper_1_pdf

Experiments show that the method was effective in predicting the political trends for the Catalonia case, but not for the Lombardy case. Among the various motivations for this, we noticed that in general Twitter was more representative of the users opposing the referendum than the ones in favor.

The work has been presented at the KDWEB workshop at the ICWE 2018 conference.

A preprint of the paper can be downloaded from ArXiv and cited as reported here:

Roberto Napoli, Ali Mert Ertugrul, Alessandro Bozzon, Marco Brambilla. A User Modeling Pipeline for Studying Polarized Political Events in Social Media. KDWeb Workshop 2018, co-located with ICWE 2018, Caceres, Spain, June 2018. arXiv:1807.09459

"What’s special about us?" Harvard computational science symposium on Brain+Computer systems

by Marco Brambilla, posted in big data

On Friday, January 22, 2016 I attended a very interesting symposium organised by Harvard University Institute for Computational Science on “BRAIN + MACHINES: EXPLORING THE FRONTIERS OF NEUROSCIENCE AND COMPUTER SCIENCE”.

Although it fell outside my main research fields, I found it very interesting and enlightening. And the discussed topics could also imply some crucial role for modelling practices.
The introductory speech by David Cox, addressed the role and span of brain studies. First, he pointed out that when we say we want to study the brain, at a deep level, we say we want to study ourselves.
Indeed, we all perceive human species is special. But why is that? We are not the biggest, longest-living, most numerous, most adapted species. We simply cover a niche, as any other species.

What’s special about us is the complexity, not in general sense (nature is plenty of complexity), but specifically complexity of our brain.
Our brain includes 100 billions neutrons, and 100 trillions connections.
We are able to deal with complex information in incredible ways, because each neuron is actually a small computer, and globally our brain is enormously more powerful than any computer built so far.
We therefore build clusters of computers. But this is still not enough to obtain the brain power, we need to understand how brain works, to treat and replicate it.
Typical and crucial problems include to study: vision and image processing, positioning and mobility, and so on.
That’s where I think modelling can play a crucial role here.
As we clearly pointed out in our book Model-driven Development in Practice, modelling and abstraction is a natural way of working for our brain. And I got confirmation from renowned luminaries from Harvard today.
I really think that, should we discover the modelling approaches of our mind, we could disclose a lot of important aspects of several research fields.
Just imagine if:

  • we could represent human brain processes through models
  • we could replicate these processes and apply modelling techniques for improving, transforming and exploiting such models.

This would pave the way to infinite applications and researches. However, one big challenge opens up for the modelling community: are we able to deal with models including trillions of items??

Any further insights on this?

If you want further details on the event, checkout the official website of the symposium here and my storified social media report here.

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