In the digital era, social media has emerged as a powerful channel for expressing opinions, but online platforms have also become a breeding ground for hate speech targeting individuals based on color, caste, gender, sexual orientation, and political ideologies. Despite growing interest in automatic hate speech detection, existing research remains predominantly focused on English, underscoring a critical need to extend efforts to under-resourced languages. To bridge this gap, the HASOC (Hate Speech and Offensive Content Identification) shared task has been promoting multilingual hate speech research. In this paper, we present a brief overview of these four shared tasks (Assamese, Bengali, Bodo and English), datasets, participating systems, and their performance across standard evaluation metrics—precision, recall, accuracy, and macro F1 score. In addition, we analyze the inter-system agreement using Cohen’s κ and Fleiss’ κ , and investigate item-level difficulty through hardness analyses. Our findings offer valuable insights into the challenges and progress in multilingual hate speech detection, particularly for low-resource languages. This paper also serves as a model for the analysis of other results of large-scale experimentation with text classification systems. • Hate Speech Detection for social media in low-resource Languages and English is analyzed based on experiments for 4 languages. • Approaches for Hate Speech Detection and their performance are compared. • Item hardness of social media posts is analyzed and patterns for low-resource languages are compared. • Inter-system agreement or similarity is analyzed using Cohen’s κ and Fleiss’ κ .

With the advancement of digital technologies and gadgets, online content has become easily accessible. At the same time, harmful content also spread widely. There are different harmful content types present on various platforms in multiple languages. The topic of harmful content is broad and covers multiple research directions. Users of platforms are affected by all of them. In research, the different forms are mostly analysed separately, e.g. misinformation, cyber-bullying and hate speech. Most research has been conducted for only one platform, for a monolingual situation or on a particular issue. Counter-measures like blocking are down-ranking can make harmful content spreaders to switch platforms and languages to continuously reach a user base. Harmful content does not only appear on social media but also on news media. Spreader share harmful content in posts, news articles, comments and hyperlinks. There is a great need to study harmful content across platforms, languages, and topics. We plan to bring the research on harmful content under one umbrella such that different approaches and novel methods can be shared. The workshop will also cover the currently ongoing issues of war and elections. We propose the workshop, DHOW: Diffusion of Harmful Content on Online Web, which brings together the research on different topics of harmful content. We expect to discuss innovative research work and future research directions. The proposed workshop is the next iteration of DHOW 2024. https://dhow-workshop.github.io previously organized at ACM WebSci 2024 in Stuttgart, Germany.

Modeling text in a numerical representation is a prime task for any Natural Language Processing downstream task such as text classification. This paper attempts to study the effectiveness of text representation schemes on the text classification task, such as aggressive text detection, a special case of Hate speech from social media. Aggression levels are categorized into three predefined classes, namely: ‘Non-aggressive’ (NAG), ‘Overtly Aggressive’ (OAG), and ‘Covertly Aggressive’ (CAG). Various text representation schemes based on BoW techniques, word embedding, contextual word embedding, sentence embedding on traditional classifiers, and deep neural models are compared on a text classification problem. The weighted F1 score is used as a primary evaluation metric. The results show that text representation using Googles’ universal sentence encoder (USE) performs better than word embedding and BoW techniques on traditional classifiers, such as SVM, while pre-trained word embedding models perform better on classifiers based on the deep neural models on the English dataset. Recent pre-trained transfer learning models like Elmo, ULMFi, and BERT are fine-tuned for the aggression classification task. However, results are not at par with the pre-trained word embedding model. Overall, word embedding using pre-trained fastText vectors produces the best weighted F1-score than Word2Vec and Glove. On the Hindi dataset, BoW techniques perform better than word embeddings on traditional classifiers such as SVM. In contrast, pre-trained word embedding models perform better on classifiers based on the deep neural nets. Statistical significance tests are employed to ensure the significance of the classification results. Deep neural models are more robust against the bias induced by the training dataset. They perform substantially better than traditional classifiers, such as SVM, logistic regression, and Naive Bayes classifiers on the Twitter test dataset.

Abstract Printing technology has evolved through the past centuries due to technological progress. Within Digital Humanities, images are playing a more prominent role in research. For mass analysis of digitized historical images, bias can be introduced in various ways. One of them is the printing technology originally used. The classification of images to their printing technology e.g. woodcut, copper engraving, or lithography requires highly skilled experts. We have developed a deep learning classification system that achieves very good results. This paper explains the challenges of digitized collections for this task. To overcome them and to achieve good performance, shallow networks and appropriate sampling strategies needed to be combined. We also show how class activation maps (CAM) can be used to analyze the results.

An evaluation study performed in Arabic language on the five web search engines Araby, Ayna, Google, MSN and Yahoo aimed to compare how good these search engines can satisfy the information needs of native Arab users on the internet in their mother tongue. The top ten search results for fifty randomly selected search queries and the descriptions of these results in the search results list were evaluated by independent jurors on a web information retrieval basis. Comparing the relevance of results descriptions presented by each search engine and the relevance of the search results themselves, a big difference was found between search results and their descriptions for all tested engines. Six usability aspects of the tested search engines where also evaluated from the jurors' perspective. Google reached the higher usability evaluation score followed by Yahoo then MSN. The two native Arabic search engines Araby and Ayna seemed to be less accepted by participated Arab internet users.