Understanding Polarized Political Events through Social Media 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 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.

Data Cleaning for Knowledge Extraction and Understanding on Social Media

 

Social media platforms let users share their opinions through textual or multimedia content. In many settings, this becomes a valuable source of knowledge that can be exploited for specific business objectives. Brands and companies often ask to monitor social media as sources for understanding the stance, opinion, and sentiment of their customers, audience and potential audience. This is crucial for them because it let them understand the trends and future commercial and marketing opportunities.

However, all this relies on a solid and reliable data collection phase, that grants that all the analyses, extractions and predictions are applied on clean, solid and focused data. Indeed, the typical topic-based collection of social media content performed through keyword-based search typically entails very noisy results.

We recently implemented a simple study aiming at cleaning the data collected from social content, within specific domains or related to given topics of interest.  We propose a basic method for data cleaning and removal of off-topic content based on supervised machine learning techniques, i.e. classification, over data collected from social media platforms based on keywords regarding a specific topic. We define a general method for this and then we validate it through an experiment of data extraction from Twitter, with respect to a set of famous cultural institutions in Italy, including theaters, museums, and other venues.

For this case, we collaborated with domain experts to label the dataset, and then we evaluated and compared the performance of classifiers that are trained with different feature extraction strategies.

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

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

Emre Calisir, Marco Brambilla. The Problem of Data Cleaning for Knowledge Extraction from Social Media. KDWeb Workshop 2018, co-located with ICWE 2018, Caceres, Spain, June 2018.

The slides used in the workshop are available online here:

 

Iterative knowledge extraction from social networks

Yesterday, we presented a new work at The Web Conference in Lyon along the research line on knowledge extraction from human generated content started with our paper “Extracting Emerging Knowledge from Social Media” presented at the WWW 2017 Conference (see also this past post).

Our motivation starts from the fact that knowledge in the world continuously evolves, and thus ontologies and knowledge bases are largely incomplete, especially regarding data belonging to the so-called long tail. Therefore, we proposed a method for discovering emerging knowledge by extracting it from social content. Once initialized by domain experts, the method is capable of finding relevant entities by means of a mixed syntactic-semantic method. The method uses seeds, i.e. prototypes of emerging entities provided by experts, for generating candidates; then, it associates candidates to feature vectors built by using terms occurring in their social content and ranks the candidates by using their distance from the centroid of seeds, returning the top candidates.

Based on this foundational idea, we explored the possibility of running our method iteratively, using the results as new seeds. In this paper we address the following research questions:

  1. How does the reconstructed domain knowledge evolve if the candidates of one extraction are recursively used as seeds?
  2. How does the reconstructed domain knowledge spread geographically?
  3. Can the method be used to inspect the past, present, and future of knowledge?
  4. Can the method be used to find emerging knowledge?

This is the presentation given at the conference:

This work was presented at The Web Conference 2018, in the Modeling Social Media (MSM) workshop.

The paper is in the official proceedings of the conference through the ACM Digital Library.

You can also find here a PDF preprint version of “Iterative Knowledge Extraction from Social Networks” by Brambilla et al.

 

How Fashionable is Digital Data-Driven Fashion?

Within the context of our data science research track, we have been involved a lot in fashion industry problems recently.

We already showcased some studies in fashion, for instance related to the analysis of the Milano Fashion Week events and their social media impact.

Starting this year, we are also involved in a research and innovation project called FaST – Fashion Sensing Technology. FaST is a project meant to design, experiment with, and implement an ICT tool that could monitor and analyze the activity of Italian emerging Fashion brands on social media. FaST aims at providing SMEs in the Fashion industry with the ability to better understand and measure the behaviours and opinions of consumers on social media, through the study of the interactions between brands and their communities, as well as support a brand’s strategic business decisions.

Given the importance of Fashion as an economic and cultural resource for Lombardy Region and Italy as a whole, the project aims at leveraging on the opportunities given by the creation of an hybrid value chain fashion-digital, in order to design a tool that would allow the codification of new organizational models. Furthermore, the project wants to promote process innovation within the fashion industry but with a customer-centric approach, as well as the design of services that could update and innovate both creative processes and the retail channel which, as of today, represents the core to the sustainability and competitiveness of brands and companies on domestic and international markets.

Within the project, we study social presence and digital / communication strategies of brands, and we will look for space for optimization. We are already crunching a lot of data and running large scale analyses on the topic. We will share our exciting results as soon as available!

 

Acknowledgements

FaST – Fashion Sensing Technology is a project supported by Regione Lombardia through the European Regional Development Fund (grant: “Smart Fashion & Design”). The project is being developed by Politecnico di Milano – Design dept. and Electronics, Information and Bioengineering dept. – in collaboration with Wemanage Group, Studio 4SIGMA, and CGNAL.

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Myths and Challenges in Knowledge Extraction and Big Data Analysis

For centuries, science (in German “Wissenschaft”) has aimed to create (“schaften”) new knowledge (“Wissen”) from the observation of physical phenomena, their modelling, and empirical validation.

Recently, a new source of knowledge has emerged: not (only) the physical world any more, but the virtual world, namely the Web with its ever-growing stream of data materialized in the form of social network chattering, content produced on demand by crowds of people, messages exchanged among interlinked devices in the Internet of Things. The knowledge we may find there can be dispersed, informal, contradicting, unsubstantiated and ephemeral today, while already tomorrow it may be commonly accepted.

Picture2The challenge is once again to capture and create consolidated knowledge that is new, has not been formalized yet in existing knowledge bases, and is buried inside a big, moving target (the live stream of online data).

The myth is that existing tools (spanning fields like semantic web, machine learning, statistics, NLP, and so on) suffice to the objective. While this may still be far from true, some existing approaches are actually addressing the problem and provide preliminary insights into the possibilities that successful attempts may lead to.

I gave a few keynote speeches on this matter (at ICEIS, KDWEB,…), and I also use this argument as a motivating class in academic courses for letting students understand how crucial is to focus on the problems related to big data modeling and analysis. The talk, reported in the slides below, explores through real industrial use cases, the mixed realistic-utopian domain of data analysis and knowledge extraction and reports on some tools and cases where digital and physical world have brought together for better understanding our society.

The presentation is available on SlideShare and are reported here below:

Analysis of user behaviour and social media content for art and culture events

In our most recent study, we analysed the user behaviour and profile, as well as the textual and visual content posted on social media for art and culture events.

The corresponding paper has been presented at CD-MAKE 2017 in Reggio Calabria on August 31st, 2017.

Nowadays people share everything on online social networks, from daily life stories to the latest local and global news and events. In our paper, we address the specific problem of user behavioural profiling in the context of cultural and artistic events.

We propose a specific analysis pipeline that aims at examining the profile of online users, based on the textual content they published online. The pipeline covers the following aspects: data extraction and enrichment, topic modeling based on LDA, dimensionality reduction, user clustering, prediction of interest, content analysis including profiling of images and subjects.

Picture1We show our approach at work for the monitoring of participation to a large-scale artistic installation that collected more than 1.5 million visitors in just two weeks (namely The Floating Piers, by Christo and Jeanne-Claude). In the paper we report our findings and discuss the pros and cons of the work.

The full paper is published by Springer in the LNCS series in volume 10410, pages 219-236.

The slides used for the presentation are available on SlideShare:

 

Urbanscope: Digital Whispers from the Urban Landscape. TedX Talk Video

Together with the Urbanscope team, we gave a TedX talk on the topics and results of the project here at Politecnico di Milano. The talk was actually given by our junior researchers, as we wanted it to be a choral performance as opposed to the typical one-man show.

The message is that cities are not mere physical and organizational devices only: they are informational landscapes where places are shaped more by the streams of data and less by the traditional physical evidences. We devise tools and analysis for understanding these streams and the phenomena they represent, in order to understand better our cities.

Two layers coexist: a thick and dynamic layer of digital traces – the informational membrane – grows everyday on top of the material layer of the territory, the buildings and the infrastructures. The observation, the analysis and the representation of these two layers combined provides valuable insights on how the city is used and lived.

You can now find the video of the talk on the official TedX YouTube channel:

Urbanscope is a research laboratory where collection, organization, analysis, and visualization of cross domain geo-referenced data are experimented.
The research team is based at Politecnico di Milano and encompasses researchers with competencies in Computing Engineering, Communication and Information Design, Management Engineering, and Mathematics.

The aim of Urbanscope is to systematically produce compelling views on urban systems to foster understanding and decision making. Views are like new lenses of a macroscope: they are designed to support the recognition of specific patterns thus enabling new perspectives.

If you enjoyed the show, you can explore our beta application at:

http://www.urbanscope.polimi.it

and discover the other data science activities we are conducting at the Data Science Lab of Politecnico, DEIB.

 

Extracting Emerging Knowledge from Social Media

Today I presented our full paper titled “Extracting Emerging Knowledge from Social Media” at the WWW 2017 conference.

The work is based on a rather obvious assumption, i.e., that knowledge in the world continuously evolves, and ontologies are largely incomplete for what concerns low-frequency data, belonging to the so-called long tail.

Socially produced content is an excellent source for discovering emerging knowledge: it is huge, and immediately reflects the relevant changes which hide emerging entities.

In the paper we propose a method and a tool for discovering emerging entities by extracting them from social media.

Once instrumented by experts through very simple initialization, the method is capable of finding emerging entities; we propose a mixed syntactic + semantic method. The method uses seeds, i.e. prototypes of emerging entities provided by experts, for generating candidates; then, it associates candidates to feature vectors, built by using terms occurring in their social content, and then ranks the candidates by using their distance from the centroid of seeds, returning the top candidates as result.

The method can be continuously or periodically iterated, using the results as new seeds.

The PDF of the full paper presented at WWW 2017 is available online (open access with Creative Common license).

You can also check out the slides of my presentation on Slideshare.

A version of the tool is available online for free use, thanks also to our partners Dandelion API and Microsoft Azure. The most recent version of the tool is available on GitHub here.

Social Media Behaviour during Live Events: the Milano Fashion Week #MFW case

Social media are getting more and more  important in the context of live events, such as fairs, exhibits, festivals, concerts, and so on,  as they play an essential role in communicating them to  fans, interest groups, and the general population. These kinds of events are geo-localized within a city or territory and are scheduled within a public calendar.

Together with the people in the Fashion in Process group of Politecnico di Milano, we studied the impact on social media of a specific scenario, the Milano Fashion Week (MFW), which is an important event in Milano for the whole fashion business.

We presented this work at the Location and the Web workshop co-located with the WWW 2017 Conference in Perth, Australia.

We focus our attention on the spreading of social content  in space, measuring the spreading of the event propagation in space. We build different clusters of fashion brands, we characterize several features of propagation in space and we correlate them to the popularity of the brand and temporal propagation.

We show that the clusters along space, time and popularity dimensions are loosely correlated, and therefore trying to  understand the dynamics of the events only based on popularity  aspects would not be appropriate.

The paper PDF is available as open access PDF online on the WWW 2017 Conference web site. You can download it here.

A subsequent paper on the temporal analysis of the same event “Temporal Analysis of Social Media Response to Live Events: The Milano Fashion Week”, focusing on Granger Causality and other measures, has been published at ICWE 2017 and is available in the proceedings by Springer.

The PowerPoint presentation is available on SlideShare.

Data Science for Good City Life

On March 10, 2017 we hosted a seminar by Daniele Quercia in the Como Campus of Politecnico di Milano, on the topic:

Good City Life

daniele-quercia-good-city-life-smartcity
Daniele Quercia

Daniele Quercia leads the Social Dynamics group at Bell Labs in Cambridge
(UK)
. He has been named one of Fortune magazine’s 2014 Data All-Stars, and spoke about “happy maps” at TED.  His research has been focusing in the area of urban informatics and received best paper awards from Ubicomp 2014 and from ICWSM 2015, and an honourable mention from ICWSM 2013. He was Research Scientist at Yahoo Labs, a Horizon senior researcher at the University of Cambridge, and Postdoctoral Associate at the department of Urban Studies and Planning at MIT. He received his PhD from UC London. His thesis was sponsored by Microsoft Research and was nominated for BCS Best British PhD dissertation in Computer Science.

His presentation will contrast the corporate smart-city rhetoric about efficiency, predictability, and security with a different perspective on the cities, which I think is very inspiring and visionary.

“You’ll get to work on time; no queue when you go shopping, and you are safe because of CCTV cameras around you”. Well, all these things make a city acceptable, but they don’t make a city great.

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Daniele is launching goodcitylife.org – a global group of like-minded people who are passionate about building technologies whose focus is not necessarily to create a smart city but to give a good life to city dwellers. The future of the city is, first and foremost, about people, and those people are increasingly networked. We will see how a creative use of network-generated data can tackle hitherto unanswered research questions. Can we rethink existing mapping tools [happy-maps]? Is it possible to capture smellscapes of entire cities and celebrate good odors [smelly-maps]? And soundscapes [chatty-maps]?

The complete video of the seminar has been streamed live on youtube and is now available online at https://www.youtube.com/watch?v=Z0IprrZ7phc&w=560&h=315 and embedded here:

The seminar was open to the public and hosted at the Polo Regionale di Como headquarters of Politecnico di Milano, located in Via Anzani 42, III floor, Como.

You can also download the Good City Life flyer.