“MaTED: Metadata-Assisted Twitter Event Detection System”
is a research paper written by Mourad Oussalah , YoungRes’s partner from the Center for Machine Vision and Signal Analysis, Faculty of ITEE, University of Oulu, Oulu, Finland.
Authors: Abhinay Pandya, Mourad Oussalah , Panos Kostakos and Ummul Fatima It was published in the International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2020)
This is the research summary:
Due to its asynchronous message-sharing and real-time capabilities, Twitter offers a valuable opportunity to detect events in a timely manner. Existing approaches for event detection have mainly focused on building a temporal profile of named entities and detecting unusually large bursts in their usage to signify an event. We extend this line of research by incorporating external knowledge bases such as DBPedia, WordNet; and exploiting specific features of Twitter for efficient event detection. We show that our system utilizing temporal, social, and Twitter-specific features yields improvement in the precision, recall, and DERate on the benchmarked Events2012 corpus compared to the stateof-the-art approaches.
“Team Oulu at SemEval-2020 Task 12: Multilingual Identification of Offensive Language, Type and Target of Twitter Post Using Translated Datasets”
is a research paper written by Md. Saroar Jahan and Mourad Oussalah, YoungRes’s partners from the University of Oulu, Oulu, Finland. It was published in SemEval-2020.
Authors: Md. Saroar Jahan and Mourad Oussalah
With the proliferation of social media platforms, anonymous discussions together with easy online access, reports on offensive content have caused serious concern to both authorities and research communities. Although there is extensive research in identifying textual offensive language from online content, the dynamic discourse of social media content, as well as the emergence of new forms of offensive language, especially in a multilingual setting, calls for future research in the issue. In this work, we tackled Task A, B, and C of Offensive Language Challenge at SemEval2020. We handled offensive language in five languages: English, Greek, Danish, Arabic, and Turkish. Specifically, we pre-processed all provided datasets and developed an appropriate strategy to handle Tasks (A, B, & C) for identifying the presence/absence, type and the target of offensive language in social media. For this purpose, we used OLID2019, OLID2020 datasets, and generated new datasets, which we made publicly available. We used the provided unsupervised machine learning implementation for automated annotated datasets and the online Google translation tools to create new datasets as well. We discussed the limitations and the success of our machine learning-based approach for all the five different languages. Our results for identifying offensive posts (Task A) yielded satisfactory accuracy of 0.92 for English, 0.81 for Danish, 0.84 for Turkish, 0.85 for Greek, and 0.89 for Arabic. For the type detection (Task B), the results are significantly higher (.87 accuracy) compared to target detection (Task C), which yields .81 accuracy. Moreover, after using automated Google translation, the overall efficiency improved by 2% for Greek, Turkish, and Danish.
“On Online Hate Speech Detection. Effects of Negated Data Construction”
is a research paper written by Mourad Oussalah , YoungRes’s partners from the University of Oulu, Oulu, Finland. It was published in the 2019 IEEE International Conference on Big Data (Big Data).
Authors: Cheniki Abderrouaf and Mourad Oussalah
Paper link: https://ieeexplore.ieee.org/document/9006336M
This is the research summary:
In the era of social media and mobile internet, the design of automatic tools for online detection of hate speech and/or abusive language becomes crucial for society and community empowerment. Nowadays of current technology in this respect is still limited and many service providers are still relying on the manual check. This paper aims to advance in this topic by leveraging novel natural language processing, machine learning, and feature engineering techniques. The proposed approach advocates a classification-like technique that makes use of a special data design procedure. The latter enforces a balanced training scheme by exploring the negativity of the original dataset. This generates new transfer learning paradigms, Two classification schemes using convolution neural network and LSTN architecture that use FastText embeddings as input features are contrasted with baseline models constituted of Logistic regression and Naives’ Bayes classifiers. Wikipedia Comment dataset constituted of Personal Attack, Aggression and Toxicity data are employed to test the validity and usefulness of the proposal.
“Mining Security discussions in Suomi24”
is a research paper written by Mourad Oussalah, YoungRes’s partners from the University of Oulu, Oulu, Finland. It was published in the 2019 European Intelligence and Security Informatics Conference (EISIC).
Authors: Eetu Haapamäki, Juho Mikkola, Markus Hirsimäki and Mourad Oussalah.
This is the research summary:
This study examines how social network based approach can be applied in order to mine the security oriented discussions in Suomi24 online forum. The approach employs a student survey questionnaire to collect a dictionary related to Finland national security. In subsequent analysis, the vocabulary terms are mapped to Suomi24 corpus in order to construct the associated social network analysis that quantifies the dependency among the various vocabulary terms. Especially, the analysis of the dynamic variation of the network topology would enable the decision-maker to devise appropriate communication scheme to maximize intervention in the public sphere and reach a wider audience. Besides, a parser that finds the keywords from VeRticalzed text data format is developed to aid the construction of the underlined social network.
Improving Youngsters’ Resilience Through Video Game-Based Interventions
Vietnam Journal of Computer Science14 Apr 2020 - OPEN ACCESS
Radicalization, as a violent form of extremism, is a growing problem for Europe. Currently, it is possible to find extreme ideologies regarding almost every topic such as religion, politics or sports. This problem, which ranges from personal identity conflicts to complex societal issues, has an impact on several people every day, especially on youngsters. To confront this situation, the European Union found several initiatives, as a way to face this problem from a scientific perspective. Some of these initiatives face the problem trying to reduce radicalization by working on personal and social skills through education, in such a way the youngster’s resilience is improved. This paper aims to present YoungRes, a European project whose goal is to improve the resilience of youngsters. To do so, it unifies an already created intervention — named Fortius — through the inclusion of video games in the learning process. This paper describes both: (1) how the Fortius program is modified to allow video games sessions and (2) the software architecture designed to allow students and educators to participate in YoungRes project. Finally, different suggestions to include in future versions of the game are discussed.