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The cross-lingual natural disaster dataset includes public tweets collected using Twitter’s public API, filtering by location-related keywords and date, without using any additional filtering (e.g., we did not restrict the query to specific languages). We considered five disaster events between January 2020 and February 2021 that received substantial news coverage internationally. All messages include a “language” field computed by Twitter us ing a language detection model developed specifically for tweets. We counted the number of messages per language in each event. Three of the top languages were common to all the studied events: English (ISO 639-1 code: en), Spanish (es), and French (fr). Additionally, we found several hundred messages for each event in other languages, including Catalan (ca), Tagalog (tl), Croatian (hr), German (de), Japanese (ja), Indonesian (id), and Portuguese (pt). After collecting the data, we labelled tweets or their translation to English that contained potentially informative factual information. We name this group of tweets “informative messages.” Next, we used crowdsourcing to further categorize the messages into various informational categories. We asked three different workers to label each of the approximately 5,700 informative messages across languages. The target categories were based on an ontology from TREC-IS 2018, where we grouped some low level ontology categories into higher-level ones. {"references": ["Vitiugin, Fedor and Castillo, Carlos (2022), Cross-Lingual Query-Based Summarization of Crisis-Related Social Media: An Abstractive Approach Using Transformers, Proc of Ht'22"]}

The dataset created for evaluation of summaries generated from social media posted during five natural disasters. The dataset contains: ground truth reports created by human assessor based on ERCC Echo Flash reports (5 events); summaries generated by extractive and abstractive state-of-the-art with manual annotation of category-relevant and crisis-relevant claim. The dataset with annotated social media postings — link The full description of used summarization methods, sources of data and evaluation metrics available in the paper — link {"references": ["Vitiugin, Fedor, & Castillo, Carlos. (2023). Dataset for Evaluating Abstractive Summaries of Crisis-Related Social Media"]}

In this article we propose a new theoretical framework to conceptualise Internet memes and to trace their temporal variation on 4chan/pol/. We draw from literature on primary and secondary orality to conceptualise the repetition-with-variation of Internet memes as a form of memecry, which we argue is specifically pertinent to the collectivity of online subcultures. We operationalise its study through formulas: mnemonic phrases that encapsulate important elements of oral cultures, which have arguably regained prominence in ephemeral and fast-paced online environments. While Internet memes have often been studied as single images or words, formulas provide a more complex unit for tracing variation and not only circulation. We offer a quali-quantitative protocol to investigate memecry and visualise the spread and variability of 65 prominent formulas on 4chan/pol/, a far-right space known for its reliance on memes. By discussing several cases, we demonstrate how 4chan’s collective identity indeed features typical of secondary oral cultures, while revealing how the memecry of its formulas is entwined with reactionary sentiments and a subcultural struggle for distinction.

Datasets underlying the analysis of the paper "Memecry: Tracing the Repetition-with-Variation of Formulas on 4chan/pol/ This upload includes the following: seedwords.csv: A .csv file with terms we used as a seed list to filter for 4chan/pol/-post containing vernacular. seedword-network_x.gdf/gephi: .gdf and .gephi network files for NPMI-weighted co-word networks of /pol/-posts. We only included posts that contained one of the aforementioned seed list words. twoflow-data_x.xlsx: .xlsx files with data on triplets common to 4chan/pol/. We identified these three-word sequences through the above network files. For example: "gr8 b8 m8", "orange man bad", "lurk moar newfag". The Excel data on these triplet includes: The absolute amount of /pol/-posts per year mentioning the triplets (within a window of five words). The average NPMI scores between the three triplet words per year. The top co-words per year having an average NPMI higher than 0.18 with two of the three triplet words. triplets.csv: A .csv file with the extracted triplets, including their common appearance as memetic phrases and a short explanation. This data was used for "two-flow graphs" available at oilab.eu/formulas/. See the paper for full explanations on the data. Sal Hagen has received a PhD in the Humanities grant from the Dutch Research Council (NWO) under grant number PGW.19.030. Tommaso Venturini author works has been supported by the European Union – Horizon 2020 Program under the scheme "INFRAIA-01-2018-2019 – Integrating Activities for Advanced Communities", Grant Agreement n.871042, "SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics" (http://www.sobigdata.eu).