Brand Community Analysis using Graph Representation Learning on Social Networks – with a Fashion Case

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.

The use case is taken from a joint research project with the Fashion in Process group in the Design Department of Politecnico di Milano, within the framework of FAST (Fashion Sensing Technology).

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.

Possible Theses in Data Science

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.

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:

 

IEEE Big Data Conference 2017: take home messages from the keynote speakers

I collected here the list of my write-ups of the first three keynote speeches of the conference:

Driving Style and Behavior Analysis based on Trip Segmentation over GPS Information through Unsupervised Learning

Over one billion cars interact with each other on the road every day. Each driver has his own driving style, which could impact safety, fuel economy and road congestion. Knowledge about the driving style of the driver could be used to encourage “better” driving behaviour through immediate feedback while driving, or by scaling auto insurance rates based on the aggressiveness of the driving style.
In this work we report on our study of driving behaviour profiling based on unsupervised data mining methods. The main goal is to detect the different driving behaviours, and thus to cluster drivers with similar behaviour. This paves the way to new business models related to the driving sector, such as Pay-How-You-Drive insurance policies and car rentals. Here is the presentation I gave on this topic:

Driver behavioral characteristics are studied by collecting information from GPS sensors on the cars and by applying three different analysis approaches (DP-means, Hidden Markov Models, and Behavioural Topic Extraction) to the contextual scene detection problems on car trips, in order to detect different behaviour along each trip. Subsequently, drivers are clustered in similar profiles based on that and the results are compared with a human-defined ground-truth on drivers classification.

The proposed framework is tested on a real dataset containing sampled car signals. While the different approaches show relevant differences in trip segment classification, the coherence of the final driver clustering results is surprisingly high.

 


This work has been published at the 4th IEEE Big Data Conference, held in Boston in December 2017. The full paper can be cited as:

M. Brambilla, P. Mascetti and A. Mauri, “Comparison of different driving style analysis approaches based on trip segmentation over GPS information,” 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, 2017, pp. 3784-3791.
doi: 10.1109/BigData.2017.8258379

You can download the full paper PDF from the IEEE Explore Library, at this url:

https://ieeexplore.ieee.org/document/8258379/

If you are interested in further contributions at the conference, here you can find my summaries of the keynote speeches on human-in-the-loop machine learning and on increasing human perception through text mining.

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:

Urban Data Science Bootcamp

We organize a crash-course on how the science of urban data can be applied to solve metropolitan issues.

crowdinsights_bootcamp_2017_en

The course is a 2 days face-to-face event with teaching sessions, workshops, case study discussions and hands-on activities for non-IT professionals in the field of city management. It is issued in two editions along the year:

  • in Milan, Italy, on  November 8th-9th, 2017
  • in Amsterdam, The Netherlands, on November 30th-December 1st, 2017.

You can download the flyer and program of the Urban datascience bootcamp 2017.

Ideal participants include: Civil servants, Professionals, Students, Urban planners, and managers of city utilities and services. No previous experience in data science or computer science is required. Attendees should have experience in areas such as economic affairs, urban development, management support, strategy & innovation, health & care, public order & safety.

Data is the catalyst needed to make the smart city vision a reality in a transparent and evidence-based (i.e. data-driven) manner. The skills required for data-driven urban analysis and design activities are diverse, and range from data collection (field work, crowdsensing, physical sensor processing, etc.); data processing by employing established big data technology frameworks; data exploration to find patterns and outliers in spatio-temporal data streams; and data visualization conveying the right information in the right manner.

The CrowdInsights professional school “Urban Data Science Bootcamp” provides a no-frills, hands-on introduction to the science of urban data; from data creation, to data analysis, data visualization and sense-making, the bootcamp will introduce more than 10 real-world application uses cases that exemplifies how urban data can be applied to solve metropolitan issues. Attendees will explore the challenges and opportunities that come from the adoption of novel types of urban data source, including social media, mobile phone data, IoT networks, etc.

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.