Dataset Information

Annotated dataset of history-related tweets.

ABSTRACT: In this article, we present a dataset containing history-related content obtained from social media. It contains hashtags and tweets that include these hashtags, as well as the results of third party tools applied to the tweets that include extracted entities, years, and url categories, and the categories for the history-related hashtags we used to crawl the tweets. We collected the tweets from Twitter official API using hashtag-based crawling. The crawling process had been performed from March 2016 to July 2018. During the crawling, we applied a bootstrapping approach which is an iterative process of collecting tweets using a small set of seed hashtags, and a manual inspection of newly acquired hashtags that co-occur with the seed hashtags to include those they are related to history. Finally, we collected 147 history-related hashtags and 2,370,252 tweets. We then defined 6 categories for the collected hashtags after their manual investigation. The presented dataset could be useful for further analysis on how people refer to history in Twitter, for collecting new history-related tweets, for training classifiers to detect history-related tweets, or for further investigations of the proposed hashtag categories.

SUBMITTER: Sumikawa Y

PROVIDER: S-EPMC8427230 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:The real-time detection of multinational banknotes remains an ongoing research challenge within the academic community. Numerous studies have been conducted to address the need for rapid and accurate banknote recognition, counterfeit detection, and identification of damaged banknotes [1], [2], [3]. State-of-the-art techniques, such as machine learning (ML) and deep learning (DL), have supplanted traditional digital image processing methods in banknote recognition and classification. However, the success of ML or DL projects critically hinges on the size and comprehensiveness of the datasets employed. Existing datasets suffer from several limitations. Firstly, there is a notable absence of a Peruvian banknote dataset suitable for training ML or DL models. Second, the lack of annotated data with specific labels and metadata for Peruvian currency hinders the development of effective supervised learning models for banknote recognition and classification. Lastly, datasets from different regions may not align with the unique characteristics, design, and security features of Peruvian banknotes, limiting the accuracy and applicability of models in a Peruvian context [4] To address these limitations, we have meticulously curated a comprehensive dataset comprising a total of 9,315 images of Peruvian banknotes, encompassing both old and new denominations from 2011 (old) and 2019 (new) [5]. The Peruvian banknote dataset includes denominations of 10, 20, 50, and 100 Peruvian soles. Importantly, as indicated by [5], both the 2011 and 2019 families of banknotes are currently in circulation, further enhancing the dataset's relevance for real-world applications in currency recognition and verification. This dataset serves as a vital resource for addressing the challenges in real-time multinational banknote detection. By offering a comprehensive collection of images of Peruvian banknotes, both old and new, this dataset fills a critical gap in the field of banknote recognition. Researchers can utilize it to train and evaluate advanced machine learning and deep learning models, ultimately enhancing the accuracy of banknote processing systems.

Dataset Information

Annotated dataset of history-related tweets.

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