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Auflistung Forschungsdaten nach Auflistung nach Fachbereich/Einrichtung "FB 05 - Sprache, Literatur, Kultur"

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    Dataset Russian Booktube collected Aug-Oct. 2021
    (2022-06-28) Hamidy, Elena
    The data set was created in August-October 2021 as part of a research project on Russian Booktube. Research results will be published in the 14th issue of the peer-reviewed Apparatus Journal (2022:14) in Russian. Data on videos were collected with youtube-dl (development status August 2021). Only officially, public-accessed data were collected. Before collection, video channels were selected through field observation and qualitative and quantitative evaluation of relevant tags. The data for each video from these channels was downloaded in three phases: on 08/23/21, 09/14/21, and 10/07/21. The files EHAMIDY_RUSBOOKTUBE_WITH CLASSIFICATION.xlsx (31,5 MB) and EHAMIDY_RUSBOOKTUBE_WITH CLASSIFICATION.csv (93,1 MB) contain basic video information downloaded on 08/23/21, and information about views downloaded on 09/14/21 and 10/07/21. In addition, the files contain a classification of the videos by titles and tags. Two visualizations are published within the dataset: the visualization of the dataset as an interactive table (EHAMIDY_Booktube_dataset_interactive_table.html) and an interactive graph (EHAMIDY_Booktube_genres.html) showing the number of videos by date and category. Because both excel- and csv-files are large and contain Cyrillic letters they may cause errors if you open them in Excel. In Python, e.g. in Jupyter Notebooks they work perfectly.
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    “Krymnash” on YouTube
    (2022-09-08) Hamidy, Elena
    The dataset was collected in three phases using snowball-sampling during 2020. The experiment was designed to simulate the situation in which users want to inform themselves about Crimea’s annexation on YouTube, and progress by watching videos relevant to the question, picking only relevant recommendations from the list of recommendations in the three stages of the experiment. For the first data collection, fifteen starting videos were manually selected by the author using the YouTube search function. They had to be relatively short (under 20 minutes), seven of them represented a clearly positive attitude towards the annexation, seven evaluated this event negatively, and one video represented an ambivalent perspective. Collected recommendations were manually annotated. Two independent annotators watched all recommended videos in full length and evaluated such parameters as relevance, evaluation of the annexation, language, topic, and category. In addition, the presence of actual topics, such as the increasing issue of the pandemic and various anniversaries, among them the anniversary of the Crimea annexation, was marked in the dataset. The second data collection starts from a selection of videos taken from the first and uses the foremost relevant recommendations, each recommended more than 12 times. For the third data collection, the recommendations were collected after watching the top of relevant recommendations (44 videos, including several watched videos from the second data collection as they were frequently recommended again). The dataset includes: 1. first dataset of collected recommendations with annotation (DATASET_1.xlsx), 2. second dataset of collected recommendations (DATASET_2.xlsx), 3. third dataset of collected recommendations (DATASET_3.xlsx), 4. Jupyter Notebook with data exploration and visualisations as JN (Krymnash_notebook_EH.jpynb, executable) and PDF (Krymnash_notebook_EH.pdf, non-executable, only readable), 5. four Gephi-graphs as visualisations of first (graph_first_dataset.gephi), second (graph_second_dataset.gephi), third (graph_third_dataset.gephi) and consolidated (1-3) data sets (graph_mutual_relations_across_datasets.gephi), 5. table with the description of stages and user variables (overview_stages_and_users.xlsx), 6. codebook for annotation of the first dataset (Codebook_annotation_dataset_1.pdf), 7. screenshots of graphs, used as visualisations in the Jupyter Notebook (category.png, engagement.png, evaluation.png, evaluation_second_ds.png, mutual_relations_network.png, relevance_third_ds.png, selection_second_ds.png) 8. properties of unique videos of all datasets (all_datasets_df_with_tags.xlsx).