Real-time social media data can provide useful information on evolving hazards. Alongside traditional methods of disaster detection, the integration of social media data can considerably enhance disaster management. In this paper, we investigate the problem of detecting geolocation-content communities on Twitter and propose a novel distributed system that provides in near real-time information on hazard-related events and their evolution. We show that content-based community analysis can lead to better and faster dissemination of hazard-related reports than using only traditional methods, such as satellite or airborne sensing platforms. Our distributed disaster reporting system analyzes the social relationship among worldwide geolocated tweets and applies topic modeling to group tweets by topics. Considering for each tweet the following information: user, timestamp, geolocation, retweets, and replies, we create a publisher-subscriber distribution model for topics. We use content similarity and the proximity of nodes to create a new model for geolocation-content based communities. Users can subscribe to different topics in specific geographical areas or worldwide and receive real-time reports regarding these topics. As misinformation can lead to increased damage if propagated in hazards-related tweets, we propose a new deep learning model to detect fake news. The misinformed tweets are then removed from display. We also show empirically the scalability capabilities of the proposed system.

With the rise of social media users and the general shift of communication from traditional media to online platforms, the spread of harmful content (e.g., hate speech, misinformation, fake news) has been exacerbated. Harmful content in the form of hate speech causes a person distress or harm, having a negative impact on the individual mental health, with even more detrimental effects on the psychology of children and teenagers. In this paper, we propose an end-to-end solution with real-time capabilities to detect harmful content in real-time and mitigate its spread over the network. Our main contribution is Sparse Shield, a novel method that out-scales existing state-of-the-art methods for network immunization. We also propose a novel architecture for harmful speech mitigation that maximizes the impact of immunization. Our solution aims to identify a set of users for which to move harmful content at the bottom of the user feed, rather than censoring users. By immunizing certain network nodes in this manner, we minimize the negative impact on the network and minimize the interference with and limitation of individual freedoms: the information is not hidden but rather not as easy to reach without an explicit search. Our analysis is based on graphs built on real-world data collected from Twitter; these graphs reflect real user behavior. We perform extensive scalability experiments to prove the superiority of our method over existing state-of-the-art network immunization techniques. We also perform extensive experiments to showcase that Sparse Shield outperforms existing techniques on the task of harmful speech mitigation on a real-world dataset.

Personalized social media offer communication opportunities that mass media could not afford, yet also raise novel challenges. A prime challenge arising from this shift in digital communication is to detect topics and events of interest. In this paper, we propose and deploy a novel Deep Learning architecture that predicts if a news topic becomes viral by analyzing social media diffusion and audience interest in current news events. The proposed solution: (i) analyzes news articles, (ii) extracts associated topics and events, (iii) matches the topics and events to filter and extract developing topics, (iv) extracts current events from Twitter and matches them to the filtered news topics, and (v) predicts audience interest in news topics using Twitter likes and retweets. We employ several feature engineering techniques to improve prediction by integrating user metadata into the training set. In our experiments, we correlate two datasets collected over several months in the same time period. The first dataset contains news articles collected from different news venues, while the second one contains tweets regarding the news. The experimental results from our real-world deployment prove that the proposed system achieves high accuracy when integrating influencers metadata and the day of the week. Thus, proving that the news topics virality prediction is improved under the assumptions that spreaders and the day of the week play a huge role in information diffusion.

The paper proposes an interdisciplinary approach including methods from disciplines such as history of concepts, linguistics, natural language processing (NLP) and Semantic Web, to create a comparative framework for detecting semantic change in multilingual historical corpora and generating diachronic ontologies as linguistic linked open data (LLOD). Initiated as a use case (UC4.2.1) within the COST Action Nexus Linguarum, European network for Web-centred linguistic data science, the study will explore emerging trends in knowledge extraction, analysis and representation from linguistic data science, and apply the devised methodology to datasets in the humanities to trace the evolution of concepts from the domain of socio-cultural transformation. The paper will describe the main elements of the methodological framework and preliminary planning of the intended workflow.