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apps Other research productkeyboard_double_arrow_right Other ORP type 2023Publisher:Taylor & Francis Funded by:EC | SoBigData-PlusPlusEC| SoBigData-PlusPlusAuthors: Hagen, Sal; Venturini, Tommaso;Hagen, Sal; Venturini, Tommaso;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.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.6084/m9.figshare.23212416.v1&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.6084/m9.figshare.23212416.v1&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Other ORP type 2022 InglésPublisher:IOS Press Funded by:EC | SoBigData-PlusPlusEC| SoBigData-PlusPlusAuthors: Arias Duart, Anna; Parés Pont, Ferran; Giménez Ábalos, Víctor; Garcia Gasulla, Dario;Arias Duart, Anna; Parés Pont, Ferran; Giménez Ábalos, Víctor; Garcia Gasulla, Dario;handle: 2117/375991
One direct application of explainable AI feature attribution methods is to be used for detecting unwanted biases. To do so, domain experts typically have to review explained inputs, checking for the presence of unwanted biases learnt by the model. However, the huge amount of samples the domain experts must review makes this task more challenging as the size of the dataset grows. In an ideal case, domain experts should be provided only with a small number of selected samples containing potential biases. The recently published Focus score seems a promising tool for the selection of samples containing potential unwanted biases. In this work, we conduct a first study in this direction, analyzing the behavior of the Focus score when applied to a biased model. First, we verified that Focus is indeed sensitive to an induced bias. This is assessed by forcing a spurious correlation, training a model using only cats-indoor and dogs-outdoor. We empirically prove that the model learnt to distinguish the contexts (outdoor vs indoor) instead of cat vs dog classes, so ensuring that the model learnt an unwanted bias. Afterwards, we apply the Focus on this biased model showing how the Focus score decreases when the input contains the aforementioned bias. This analysis sheds light on the Focus behavior when applied to a biased model, highlighting its strengths for its use for bias detection. This work is supported by the European Union – H2020 Program under the “INFRAIA-01-2018-2019 – Integrating Activities for Advanced Communities”, Grant Agreement n.871042, “SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics” and by the Departament de Recerca i Universitats of the Generalitat de Catalunya under the Industrial Doctorate Grant DI 2018-100. Peer Reviewed
Recolector de Cienci... arrow_drop_down Recolector de Ciencia Abierta, RECOLECTAOther ORP type . 2022 . Peer-reviewedData sources: Recolector de Ciencia Abierta, RECOLECTAadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=2117/375991&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert Recolector de Cienci... arrow_drop_down Recolector de Ciencia Abierta, RECOLECTAOther ORP type . 2022 . Peer-reviewedData sources: Recolector de Ciencia Abierta, RECOLECTAadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=2117/375991&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu
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apps Other research productkeyboard_double_arrow_right Other ORP type 2023Publisher:Taylor & Francis Funded by:EC | SoBigData-PlusPlusEC| SoBigData-PlusPlusAuthors: Hagen, Sal; Venturini, Tommaso;Hagen, Sal; Venturini, Tommaso;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.
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.6084/m9.figshare.23212416.v1&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.6084/m9.figshare.23212416.v1&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euapps Other research productkeyboard_double_arrow_right Other ORP type 2022 InglésPublisher:IOS Press Funded by:EC | SoBigData-PlusPlusEC| SoBigData-PlusPlusAuthors: Arias Duart, Anna; Parés Pont, Ferran; Giménez Ábalos, Víctor; Garcia Gasulla, Dario;Arias Duart, Anna; Parés Pont, Ferran; Giménez Ábalos, Víctor; Garcia Gasulla, Dario;handle: 2117/375991
One direct application of explainable AI feature attribution methods is to be used for detecting unwanted biases. To do so, domain experts typically have to review explained inputs, checking for the presence of unwanted biases learnt by the model. However, the huge amount of samples the domain experts must review makes this task more challenging as the size of the dataset grows. In an ideal case, domain experts should be provided only with a small number of selected samples containing potential biases. The recently published Focus score seems a promising tool for the selection of samples containing potential unwanted biases. In this work, we conduct a first study in this direction, analyzing the behavior of the Focus score when applied to a biased model. First, we verified that Focus is indeed sensitive to an induced bias. This is assessed by forcing a spurious correlation, training a model using only cats-indoor and dogs-outdoor. We empirically prove that the model learnt to distinguish the contexts (outdoor vs indoor) instead of cat vs dog classes, so ensuring that the model learnt an unwanted bias. Afterwards, we apply the Focus on this biased model showing how the Focus score decreases when the input contains the aforementioned bias. This analysis sheds light on the Focus behavior when applied to a biased model, highlighting its strengths for its use for bias detection. This work is supported by the European Union – H2020 Program under the “INFRAIA-01-2018-2019 – Integrating Activities for Advanced Communities”, Grant Agreement n.871042, “SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics” and by the Departament de Recerca i Universitats of the Generalitat de Catalunya under the Industrial Doctorate Grant DI 2018-100. Peer Reviewed
Recolector de Cienci... arrow_drop_down Recolector de Ciencia Abierta, RECOLECTAOther ORP type . 2022 . Peer-reviewedData sources: Recolector de Ciencia Abierta, RECOLECTAadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=2117/375991&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu0 citations 0 popularity Average influence Average impulse Average Powered by BIP!more_vert Recolector de Cienci... arrow_drop_down Recolector de Ciencia Abierta, RECOLECTAOther ORP type . 2022 . Peer-reviewedData sources: Recolector de Ciencia Abierta, RECOLECTAadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://www.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=2117/375991&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu