No abstract

The Internet is a communication space where newly formed communities search for ways to reflect on their social nature. We provide a theoretical background to demonstrate how the humor was used to manipulate social groups before the rise of mass media and after it. We use Critical Discourse Analysis and pragmatics to study several cases of social manipulation with the help of humor. The two Internet communities, 2ch and Pikabu, being among the largest Russian-speaking entertainment communities, often compete and use humor as a way to manipulate their representatives for social purposes: to consolidate, fight back, reflect on their community's norms and values. Our research shows that these communities follow the old traditions of humor and laughter to organize the poorly regulated information space. Although 2chers tend to use trolling more often, there are no general differences between these communities in how they use humor to manipulate their social group.

In 2014‒2016 field seasons, bird censuses were conducted on 34 flyways in seven different types of open habitats of the Russian part of the Ishim River region. Ninety-five species from 72 genera from 10 orders were registered. It is established that the taxonomic composition and ecological structure of avifauna of the habitats under investigation comply with their biotopical characteristics; diversity of taxons and ecological groups show positive correlation with habitats’ heterogeneity. In natural habitats, the maximum total abundance of birds, highest species diversity within the habitat (α-diversity) and species sustainability are characteristic of river meadows ornithocenoses, mainly due to low-numbered species and a higher evenness index. In disturbed habitats, the maximum total abundance, species diversity, Shannon diversity index, Pielou’s evenness index, minimal index of diversity and highest indices of elastic and general sustainability are characteristic of abandoned fields ornithocenoses, due to a more complex structure of vegetation communities and habitat resource capacity, which increased in the course of secondary succession. Due to natural and historical unity, middle and northern forest steppe’s avifaunae are most similar. The southern taiga open habitats’ ornithocenoses are most heterogeneous, due to an increased amount of dendrophilous birds along with forest habitats’ increased area and diversity.

The article describes a fast solution to propaganda detection at SemEval-2020 Task 11, based on feature adjustment. We use per-token vectorization of features and a simple Logistic Regression classifier to quickly test different hypotheses about our data. We come up with what seems to us the best solution, however, we are unable to align it with the result of the metric suggested by the organizers of the task. We test how our system handles class and feature imbalance by varying the number of samples of two classes (Propaganda and None) in the training set, the size of a context window in which a token is vectorized and combination of vectorization means. The result of our system at SemEval2020 Task 11 is F-score=0.37. This work is licensed under a Creative Commons Attribution 4.0 International Licence. Licence details: http:// creativecommons.org/licenses/by/4.0/.1 https://www.datasciencesociety.net/hack-news-datathon/ 2 These 18 techniques have grown from the 1930s American Institute of Propaganda Analysis materials (Miller, 1939) and more recent investigations both into the tools of propaganda (Torok, 2015; Teninbaum, 2009, and others), and into the rules of good argument (Weston, 2018).

The article describes a fast solution to propaganda detection at SemEval-2020 Task 11, based on feature adjustment. We use per-token vectorization of features and a simple Logistic Regression classifier to quickly test different hypotheses about our data. We come up with what seems to us the best solution, however, we are unable to align it with the result of the metric suggested by the organizers of the task. We test how our system handles class and feature imbalance by varying the number of samples of two classes (Propaganda and None) in the training set, the size of a context window in which a token is vectorized and combination of vectorization means. The result of our system at SemEval2020 Task 11 is F-score=0.37. This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http:// creativecommons.org/licenses/by/4.0/.1 https://www.datasciencesociety.net/hack-news-datathon/ 2 These 18 techniques have grown from the 1930s American Institute of Propaganda Analysis materials (Miller, 1939) and more recent investigations both into the tools of propaganda (Torok, 2015; Teninbaum, 2009, and others), and into the rules of good argument (Weston, 2018).