No abstract

With the growth of social media, the spread of hate speech is also increasing rapidly. Social media are widely used in many countries. Also Hate Speech is spreading in these countries. This brings a need for multilingual Hate Speech detection algorithms. Much research in this area is dedicated to English at the moment. The HASOC track intends to provide a platform to develop and optimize Hate Speech detection algorithms for Hindi, German and English. The dataset is collected from a Twitter archive and pre-classified by a machine learning system. HASOC has two sub-task for all three languages: task A is a binary classification problem (Hate and Not Offensive) while task B is a fine-grained classification problem for three classes (HATE) Hate speech, OFFENSIVE and PROFANITY. Overall, 252 runs were submitted by 40 teams. The performance of the best classification algorithms for task A are F1 measures of 0.51, 0.53 and 0.52 for English, Hindi, and German, respectively. For task B, the best classification algorithms achieved F1 measures of 0.26, 0.33 and 0.29 for English, Hindi, and German, respectively. This article presents the tasks and the data development as well as the results. The best performing algorithms were mainly variants of the transformer architecture BERT. However, also other systems were applied with good success.

Magnetic resonance spectroscopy (MRS) can benefit from increased signal-to-noise ratio (SNR) of high magnetic fields. In this work, the SNR gain of dynamic 31P MRS at 7 T was invested in temporal and spatial resolution. Using conventional slice selective excitation combined with localization by adiabatic selective refocusing (semi-LASER) with short echo time (TE = 23 ms), phosphocreatine quantification in a 38 mL voxel inside a single exercising muscle becomes possible from single acquisitions, with SNR = 42 ± 4 in resting human medial gastrocnemius.The method was used to quantify the phosphocreatine time course during 5 min of plantar flexion exercise and recovery with a temporal resolution of 6 s (the chosen repetition time for moderate T1 saturation). Quantification of inorganic phosphate and pH required accumulation of consecutively acquired spectra when (resting) Pi concentrations were low. The localization performance was excellent while keeping the chemical shift displacement acceptably small. The SNR and spectral line widths with and without localization were compared between 3 T and 7 T systems in phantoms and in vivo.The results demonstrate that increased sensitivity of ultra-high field can be used to dynamically acquire metabolic information from a clearly defined region in a single exercising muscle while reaching a temporal resolution previously available with MRS in non-localizing studies only. The method may improve the interpretation of dynamic muscle MRS data. Magn Reson Med, 2011. © 2011 Wiley-Liss, Inc.

We describe the fourth edition of the CheckThat! Lab, part of the 2021 Cross-Language Evaluation Forum (CLEF). The lab evaluates technology supporting various tasks related to factuality, and it is offered in Arabic, Bulgarian, English, and Spanish. Task 1 asks to predict which tweets in a Twitter stream are worth fact-checking (focusing on COVID-19). Task 2 asks to determine whether a claim in a tweet can be verified using a set of previously fact-checked claims. Task 3 asks to predict the veracity of a target news article and its topical domain. The evaluation is carried out using mean average precision or precision at rank k for the ranking tasks, and F1 for the classification tasks.

The widespread of offensive content online such as hate speech poses a growing societal problem. AI tools are necessary for supporting the moderation process at online platforms. For the evaluation of these identification tools, continuous experimentation with data sets in different languages are necessary. The HASOC track (Hate Speech and Offensive Content Identification) is dedicated to develop benchmark data for this purpose. This paper presents the HASOC subtrack for English, Hindi, and Marathi. The data set was assembled from Twitter. This subtrack has two sub-tasks. Task A is a binary classification problem (Hate and Not Offensive) offered for all three languages. Task B is a fine-grained classification problem for three classes (HATE) Hate speech, OFFENSIVE and PROFANITY offered for English and Hindi. Overall, 652 runs were submitted by 65 teams. The performance of the best classification algorithms for task A are F1 measures 0.91, 0.78 and 0.83 for Marathi, Hindi and English, respectively. This overview presents the tasks and the data development as well as the detailed results. The systems submitted to the competition applied a variety of technologies. The best performing algorithms were mainly variants of transformer architectures.