3,376 research outputs found

    Digital libraries: The challenge of integrating instagram with a taxonomy for content management

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    Interoperability and social implication are two current challenges in the digital library (DL) context. To resolve the problem of interoperability, our work aims to find a relationship between the main metadata schemas. In particular, we want to formalize knowledge through the creation of a metadata taxonomy built with the analysis and the integration of existing schemas associated with DLs. We developed a method to integrate and combine Instagram metadata and hashtags. The final result is a taxonomy, which provides innovative metadata with respect to the classification of resources, as images of Instagram and the user-generated content, that play a primary role in the context of modern DLs. The possibility of Instagram to localize the photos inserted by users allows us to interpret the most relevant and interesting informative content for a specific user type and in a specific location and to improve access, visibility and searching of library content

    Towards Understanding Political Interactions on Instagram

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    Online Social Networks (OSNs) allow personalities and companies to communicate directly with the public, bypassing filters of traditional medias. As people rely on OSNs to stay up-to-date, the political debate has moved online too. We witness the sudden explosion of harsh political debates and the dissemination of rumours in OSNs. Identifying such behaviour requires a deep understanding on how people interact via OSNs during political debates. We present a preliminary study of interactions in a popular OSN, namely Instagram. We take Italy as a case study in the period before the 2019 European Elections. We observe the activity of top Italian Instagram profiles in different categories: politics, music, sport and show. We record their posts for more than two months, tracking "likes" and comments from users. Results suggest that profiles of politicians attract markedly different interactions than other categories. People tend to comment more, with longer comments, debating for longer time, with a large number of replies, most of which are not explicitly solicited. Moreover, comments tend to come from a small group of very active users. Finally, we witness substantial differences when comparing profiles of different parties.Comment: 5 pages, 8 figure

    Using graph theory and social media data to assess cultural ecosystem services in coastal areas: Method development and application

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    The use of social media (SM) data has emerged as a promising tool for the assessment of cultural ecosystem services (CES). Most studies have focused on the use of single SM platforms and on the analysis of photo content to assess the demand for CES. Here, we introduce a novel methodology for the assessment of CES using SM data through the application of graph theory network analyses (GTNA) on hashtags associated to SM posts and compare it to photo content analysis. We applied the proposed methodology on two SM platforms, Instagram and Twitter, on three worldwide known case study areas, namely Great Barrier Reef, Galapagos Islands and Easter Island. Our results indicate that the analysis of hashtags through graph theory offers similar capabilities to photo content analysis in the assessment of CES provision and the identification of CES providers. More importantly, GTNA provides greater capabilities at identifying relational values and eudaimonic aspects associated to nature, elusive aspects for photo content analysis. In addition, GTNA contributes to the reduction of the interpreter's bias associated to photo content analyses, since GTNA is based on the tags provided by the users themselves. The study also highlights the importance of considering data from different social media platforms, as the type of users and the information offered by these platforms can show different CES attributes. The ease of application and short computing processing times involved in the application of GTNA makes it a cost-effective method with the potential of being applied to large geographical scales.Comment: 23 pages, 5 figures, 2 appendice

    Using graph theory and social media data to assess cultural ecosystem services in coastal areas: Method development and application

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    The use of social media (SM) data has emerged as a promising tool for the assessment of cultural ecosystem services (CES). Most studies have focused on the use of single SM platforms and on the analysis of photo content to assess the demand for CES. Here, we introduce a novel methodology for the assessment of CES using SM data through the application of graph theory network analyses (GTNA) on hashtags associated to SM posts and compare it to photo content analysis. We applied the proposed methodology on two SM platforms, Instagram and Twitter, on three worldwide known case study areas, namely Great Barrier Reef, Galapagos Islands and Easter Island. Our results indicate that the analysis of hashtags through graph theory offers similar capabilities to photo content analysis in the assessment of CES provision and the identification of CES providers. More importantly, GTNA provides greater capabilities at identifying relational values and eudaimonic aspects associated to nature, elusive aspects for photo content analysis. In addition, GTNA contributes to the reduction of the interpreter’s bias associated to photo content analyses, since GTNA is based on the tags provided by the users themselves. The study also highlights the importance of considering data from different SM platforms, as the type of users and the information offered by these platforms can show different CES attributes. The ease of application and relative short computing processing times involved in the application of GTNA makes it a cost-effective method with the potential of being applied to large geographical scalesThis work is a result of the ECOMAR Network, “Evaluation and monitoring of marine ecosystem services in Iberoamérica” (project number 417RT0528) funded by the CYTED program. During the time of the study and writing period ARF was supported by a H2020-Marie Skłodowska-Curie Action MSCA-IF-2014 (ref. 655475); AOA was supported by a H2020-Marie Skłodowska-Curie Action MSCA-IF-2016 (ref. 746361); SdJ was supported by a H2020-Marie Skłodowska-Curie Action MSCA-IF-2016 (ref. 743545); PP was funded by the Xunta de Galicia (RECREGES II Project, Grant ED481B2018/ 017)S

    A Graph Theory approach to assess nature’s contribution to people at a global scale

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    The use of Graph Theory on social media data is a promising approach to identify emergent properties of the complex physical and cognitive interactions that occur between humans and nature. To test the effectivity of this approach at global scales, Instagram posts from fourteen natural areas were selected to analyse the emergent discourse around these areas. The fourteen areas, known to provide key recreational, educational and heritage values, were investigated with different centrality metrics to test the ability of Graph Theory to identify variability in ecosystem social perceptions and use. Instagram data (i.e., hashtags associated to photos) was analysed with network centrality measures to characterise properties of the connections between words posted by social media users. With this approach, the emergent properties of networks of hashtags were explored to characterise visitors’ preferences (e.g., cultural heritage or nature appreciation), activities (e.g., diving or hiking), preferred habitats and species (e.g., forest, beach, penguins), and feelings (e.g., happiness or place identity). Network analysis on Instagram hashtags allowed delineating the users’ discourse around a natural area, which provides crucial information for effective management of popular natural spaces for peopleThis work is a product of ECOMAR research network (Evaluation and monitoring of marine ecosystem services in Iberoamerica; project number 417RT0528, funded by CYTED). Three co-authors were funded by H2020-Marie Skłodowska-Curie Action during the conduction of this work: SdJ, funded by MSCA-IF-2016 (ref. 743545); AOA, funded by MSCA-IF-2016 (ref. 746361); ARF, funded by MSCA-IF-2014 (ref. 655475)S

    Captured and captioned: Representing family life on Instagram

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    This article examines how practices of family photography are being transformed in the digital sphere, specifically on Instagram. While research on ‘digital intimacies’ focusses on romantic or peer interactions, the digital practices of families – especially intergenerational interactions – remain understudied. We use Janet Finch’s notion of ‘family display’ to consider how Instagram affords new modes of performing and sharing family life. This concept has exciting potential for media-rich online spaces, but so far, only a few studies examine how social media platforms extend the display of family practices. To explore family photography on Instagram, we analyse a sample of 200 Instagram posts. We argue that features specific to photo-sharing in digital spaces, such as hashtags, emojis and captions, open up new aspects of and audiences for family display. Our analysis paves the way for future research about how relationships are displayed across a range of digital platforms.info:eu-repo/semantics/publishedVersio

    Learning to Hash-tag Videos with Tag2Vec

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    User-given tags or labels are valuable resources for semantic understanding of visual media such as images and videos. Recently, a new type of labeling mechanism known as hash-tags have become increasingly popular on social media sites. In this paper, we study the problem of generating relevant and useful hash-tags for short video clips. Traditional data-driven approaches for tag enrichment and recommendation use direct visual similarity for label transfer and propagation. We attempt to learn a direct low-cost mapping from video to hash-tags using a two step training process. We first employ a natural language processing (NLP) technique, skip-gram models with neural network training to learn a low-dimensional vector representation of hash-tags (Tag2Vec) using a corpus of 10 million hash-tags. We then train an embedding function to map video features to the low-dimensional Tag2vec space. We learn this embedding for 29 categories of short video clips with hash-tags. A query video without any tag-information can then be directly mapped to the vector space of tags using the learned embedding and relevant tags can be found by performing a simple nearest-neighbor retrieval in the Tag2Vec space. We validate the relevance of the tags suggested by our system qualitatively and quantitatively with a user study
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