We discuss the use of hierarchical transformers for user semantic similarity in the context of analyzing users' behavior and profiling social media users. The objectives of the research include finding the best model for computing semantic user similarity, exploring the use of transformer-based models, and evaluating whether the embeddings reflect the desired similarity concept and can be used for other tasks.
Tag: social media
The VaccinEU dataset of COVID-19 Vaccine Conversations on Twitter in French, German, and Italian
Despite the increasing limitations for unvaccinated people, in many European countries, there is still a non-negligible fraction of individuals who refuse to get vaccinated against SARS-CoV-2, undermining governmental efforts to eradicate the virus. Within the PERISCOPE project, we studied the role of online social media in influencing individuals' opinions about getting vaccinated by designing a … Continue reading The VaccinEU dataset of COVID-19 Vaccine Conversations on Twitter in French, German, and Italian
Large-Scale Analysis of On-line Conversation about Vaccines before COVID-19
Frequent words and co-occurrences used by pro-vaccination and anti-vaccination communities. In this study, we map the Twitter discourse around vaccinations in English along four years, in order to: discover the volumes and trends of the conversation; compare the discussion on Twitter with newspapers’ content; and classify people as pro- or anti- vaccination and explore how … Continue reading Large-Scale Analysis of On-line Conversation about Vaccines before COVID-19
Content-based Classification of Political Inclinations of Twitter Users
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 … Continue reading Content-based Classification of Political Inclinations of Twitter Users
News Sharing Behaviour on Twitter. A Dataset and a Pipeline
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, … Continue reading News Sharing Behaviour on Twitter. A Dataset and a Pipeline
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 … Continue reading Understanding Polarized Political Events through Social Media Analysis
Iterative knowledge extraction from social networks
Our motivation starts from the fact that knowledge in the world continuously evolves, and thus ontologies and knowledge bases are largely incomplete. We explored iterative methods, using the results as new seeds. In this paper we address the following research questions:
How does the reconstructed domain knowledge evolve if the candidates of one extraction are recursively used as seeds?
How does the reconstructed domain knowledge spread geographically?
Can the method be used to inspect the past, present, and future of knowledge?
Can the method be used to find emerging knowledge?
How Fashionable is Digital Data-Driven Fashion?
FaST – Fashion Sensing Technology - 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.
Extracting Emerging Knowledge from Social Media
Knowledge in the world continuously evolves, and ontologies are largely incomplete.
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.
Social Media Behaviour during Live Events: the Milano Fashion Week #MFW case
We study spreading of social content in space during live events, measuring the spreading of the event propagation in space. We build didifferent 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.