We just published our new MOOC “Data Science for Business Innovation” on Coursera!
Our course is available for free on Coursera and is jointly offered by Politecnico di Milano and EIT Digital, as a compendium of the must-have expertise in data science for non-technical people, including executives, middle-managers to foster data-driven innovation.
The course is an introductory, non-technical overview of the concepts of data science.
You can enrol in the first edition of the course starting today.
The course is completely free and you can enjoy content at any time, with professional English speakers and animated, engaging materials.
Here is a short intro to the course:
The course consists of introductory lectures spanning big data, machine learning, data valorization and communication.
All the remaining details can be found on Coursera:
Topics cover the essential concepts and intuitions on data needs, data analysis, machine learning methods, respective pros and cons, and practical applicability issues. The course covers terminology and concepts, tools and methods, use cases and success stories of data science applications.
The course explains what is Data Science and why it is so hyped. It discusses the value that Data Science can create, the main classes of problems that Data Science can solve, the difference is between descriptive, predictive and prescriptive analytics, and the roles of machine learning and artificial intelligence.
From a more technical perspective, the course covers supervised, unsupervised and semi-supervised methods, and explains what can be obtained with classification, clustering, and regression techniques. It discusses the role of NoSQL data models and technologies, and the role and impact of scalable cloud-based computation platforms.
All topics are covered with example-based lectures, discussing use cases, success stories and realistic examples.
If you are interested in these topics, feel free to look at it on Coursera.
We look forward to seeing you there!
Social networks are huge continuous sources of information that can be used to analyze people’s behavior and thoughts.
Our goal is to extract such information and predict political inclinations of users.
In particular, we investigate the importance of syntactic features of texts written by users when they post on social media. Our hypothesis is that people belonging to the same political party write in similar ways, thus they can be classified properly on the basis of the words that they use.
We analyze tweets because Twitter is commonly used in Italy for discussing about politics; moreover, it provides an official API that can be easily exploited for data extraction. Many classifiers were applied to different kinds of features and NLP vectorization methods in order to obtain the best method capable of confirming our hypothesis.
To evaluate their accuracy, a set of current Italian deputies with consistent activity in Twitter has been selected as ground truth, and we have then predicted their political party. Using the results of our analysis, we also got interesting insights into current Italian politics. Here are the clusters of users:
Results in understanding political alignment are quite good, as reported in the confusion matrix here:
Our study is described in detail in the paper published in the IEEE Big Data 2018 conference and linked at:
You can also look at the slides on SlideShare:
You can cite the paper as follows:
M. Di Giovanni, M. Brambilla, S. Ceri, F. Daniel and G. Ramponi, “Content-based Classification of Political Inclinations of Twitter Users,” 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 2018, pp. 4321-4327.
Online social media are changing the news industry and revolutionizing the traditional role of journalists and newspapers. In this scenario, investigating the behaviour of users in relationship to news sharing is relevant, as it provides means for understanding the impact of online news, their propagation within social communities, their impact on the formation of opinions, and also for effectively detecting individual stance relative to specific news or topics, as well as for understanding the role of journalism today.
Our contribution is two-fold.
First, we build a robust pipeline for collecting datasets describing news sharing; the pipeline takes as input a list of news sources and generates a large collection of articles, of the accounts that provide them on the social media either directly or by retweeting, and of the social activities performed by these accounts.
The dataset is published on Harvard Dataverse:
Second, we also provide a large-scale dataset that can be used to study the social behavior of Twitter users and their involvement in the dissemination of news items. Finally we show an application of our data collection in the context of political stance classification and we suggest other potential usages of the presented resources.
The code is published on GitHub:
The details of our approach is published in a paper at ICWSM 2019 accessible online.
You can cite the paper as:
Giovanni Brena, Marco Brambilla, Stefano Ceri, Marco Di Giovanni, Francesco Pierri, Giorgia Ramponi. News Sharing User Behaviour on Twitter: A Comprehensive Data Collection of News Articles and Social Interactions. AAAI ICWSM 2019, pp. 592-597.
Slides are on Slideshare:
You can also download a summary poster.
In a world more and more connected, new and complex interaction patterns can be extracted in the communication between people.
This is extremely valuable for brands that can better understand the interests of users and the trends on social media to better target their products. In this paper, we aim to analyze the communities that arise around commercial brands on social networks to understand the meaning of similarity, collaboration, and interaction among users.
We exploit the network that builds around the brands by encoding it into a graph model. We build a social network graph, considering user nodes and friendship relations; then we compare it with a heterogeneous graph model, where also posts and hashtags
are considered as nodes and connected to the different node types; we finally build also a reduced network, generated by inducing direct user-to-user connections through the intermediate nodes (posts and hashtags). These different variants are encoded using graph representation learning, which generates a numerical vector for each node. Machine learning techniques are applied to these vectors to extract valuable insights for each user and for the communities they belong to.
We report on our experiments performed on an emerging fashion brand on Instagram, and we show that our approach is able to discriminate potential customers for the brand, and to highlight meaningful sub-communities composed by users that share the same kind of content on social networks.
This study has been published by Springer as part of ACM SAC 2019, Cyprus.
Here is the slideset presenting the idea:
The paper can be referenced as:
Marco Brambilla, Mattia Gasparini: Brand Community Analysis On Social Networks Using Graph Representation Learning. ACM Symposium on Applied Computing (SAC) 2019, pp. 2060-2069.
The link to the officially published paper in the ACM Library will be available shortly.
A few days ago, politico.eu published a preview of the document that the European Union will issue as guidance for ethical issues related to artificial intelligence and machine learning.
The document was written by the High-level Expert Group on Artificial Intelligence, appointed by the European Commission.
This advanced version of the document is available online now for a sneak peek preview.
The official version will be released shortly.
Besides the actual and technical content, this step is important as a principle too, because rarely a governmental institution feels the need to take such positions on scientific/technical evolution. This pronouncement makes it clear how strategic and crucial AI and ML is deemed today, also from a political perspective.
If you want to read more about Europe’s take on AI, you can also read this article on Medium.
Topic modeling techniques have been applied in many scenarios in recent years, spanning textual content, as well as many different data sources. The existing researches in this field continuously try to improve the accuracy and coherence of the results. Some recent works propose new methods that capture the semantic relations between words into the topic modeling process, by employing vector embeddings over knowledge bases.
In our recent paper presented at the AAAI-MAKE Spring Symposium 2019, held at Stanford University, we studied how knowledge graph embeddings affect topic modeling performance on textual content. In particular, the objective of the work is to determine which aspects of knowledge graph embedding have a significant and positive impact on the accuracy of the extracted topics.
We improve the state of the art by integrating some avanced graph embedding approaches (specifically designed for knowledge graphs) within the topic extraction process.
We also studied how the knowledge base could be expanded by using dataset-specific relations between the words.
We implemented the method and we validated it with a set of experiments with 2 variations of the knowledge base, 7 embedding methods, and 2 methods for incorporation of the embeddings into the topic modeling framework, also considering different parameterizations of topic number and embedding dimensionality.
Besides the specific technical results, the work has also aims at showing the potentials of integrating statistical methods with knowledge-centric methods. The full extent of the impact of these techniques shall be explored further in the future.
The details of the work are reported in the paper, which is available online here, and in the slides, also available online (on SlideShare and here below).
Here is a presentation that summarizes some of the relevant topics currently available for theses within the Data Science Lab under my supervision.
Feel free to get in touch in case you are interested.
We implemented an analysis (meaning both a method and a system) that aim to gauge local support for the two major US political parties in the 68 most competitive House of Representative districts during the 2018 U.S. mid-term elections.
The analysis attempts to mirror the “Generic Ballot” poll, i.e., a survey of voters of a particular district which aims to measure local popularity of national parties by querying participants on the likelihood they would vote for a “generic” Democrat or Republican candidate. We collect the tweets containing national parties and politicians in the 68 most competitive districts. By most competitive we mean that they are rated as: toss up, 50%-50%, or lean by the Cook Political Report.
This means we are addressing an extremely challenging analysis and prediction problem, while disregarding the simpler cases (everyone is good at predicting the obvious!).
Our solution employs the Twitter Search API to query for tweets mentioning a national leader or party, posted form a limited geographic region (i.e., each specific congressional district). For example, the following query extracts tweets on Republicans:
To limit the search to each congressional district, we use the geocode field in the search query of the API, which queries a circular area based on the coordinates of the center and the radius. Because of the irregular shape of the congressional districts, multiple queries are needed for each of them, therefore we built a custom set of bubbles that approximate the district shape.
For the analysis of the tweets, we adopted a Recurrent Neural Network, namely a RNN-LSTM binary classifier trained on tweets.
To build training and testing data we collected tweets of users with clear political affiliation, including candidates, political activists, and also lesser know users, well versed in the political vernacular.
The accounts selected yielded around 280,000 tweets in 6 months before election day, labeled based on the author’s political affiliation.
Notice that the method is a general political-purpose language-independent analysis framework, that can be applied to any national or local context.
Further details and the results can be found on this Medium post.
You can also download a poster format reporting the work:
In case you want to cite the work, you can do it in this way:
A. Lopardo and M. Brambilla, “Analyzing and Predicting the US Midterm Elections on Twitter with Recurrent Neural Networks,” 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 2018, pp. 5389–5391.
The online running prototype, the full description of the project, its results, and source code are available at http://www.twitterpoliticalsentiment.com/USA/.
For the second time, we are proposing a “night-time” multidisciplinary interactive mini-course that introduces data science concepts, methods and use cases to bachelor students (and master students of different faculties such as management, design, architecture, and so on) of Politecnico di Milano.
The full program of the mini-course is:
|4/2/2019||Intro to big data and data science.(Re)descovering SQL.||Ceri / Brambilla||Seminari||Intro|
|5/2/2019||Big Data and NoSQL.||Brambilla||Conferenze||NoSQL overview|
|8/2/2019||Data Analysis: dimensionality, clustering||Brambilla||Seminari||Dimensionality Reduction & Clustering|
|12/2/2019||Data analysis: classification & hands-on on machine learning, AI, neural networks, deep learning||Brambilla/ Ramponi/ Di Giovanni||Conferenze||Classification, neural networks, CNN, RNN, DNN, Deep learning|
|14/2/2019||Hands-on data analysis||Ramponi/ Di Giovanni||Seminari||Python-datascienceNN-Keras|
|20/2/2019||Scenarios: Genomics, Bots and Fake News||Ceri /Daniel||Seminari||Bots and fake news|
|21/2/2019||Statistics in practice||Vantini||Seminari|
|27/2/2019||Data visualization||Ciuccarelli||Seminari||Datascience Challenges|
The course is in Italian, with teaching materials in English.
Classes are always from 5:30pm to 7:00pm.
You can read more at:
Or you can get in touch if you want more details: firstname.lastname@example.org.
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.
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.
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