Pub Date : 2024-02-14DOI: 10.3389/fcomm.2024.1281407
Debbie Loakes
This study provides an update on an earlier study in the “Capturing Talk” research topic, which aimed to demonstrate how automatic speech recognition (ASR) systems work with indistinct forensic-like audio, in comparison with good-quality audio. Since that time, there has been rapid technological advancement, with newer systems having access to extremely large language models and having their performance proclaimed as being human-like in accuracy. This study compares various ASR systems, including OpenAI’s Whisper, to continue to test how well automatic speaker recognition works with forensic-like audio. The results show that the transcription of a good-quality audio file is at ceiling for some systems, with no errors. For the poor-quality (forensic-like) audio, Whisper was the best performing system but had only 50% of the entire speech material correct. The results for the poor-quality audio were also generally variable across the systems, with differences depending on whether a .wav or .mp3 file was used and differences between earlier and later versions of the same system. Additionally, and against expectations, Whisper showed a drop in performance over a 2-month period. While more material was transcribed in the later attempt, more was also incorrect. This study concludes that forensic-like audio is not suitable for automatic analysis.
{"title":"Automatic speech recognition and the transcription of indistinct forensic audio: how do the new generation of systems fare?","authors":"Debbie Loakes","doi":"10.3389/fcomm.2024.1281407","DOIUrl":"https://doi.org/10.3389/fcomm.2024.1281407","url":null,"abstract":"This study provides an update on an earlier study in the “Capturing Talk” research topic, which aimed to demonstrate how automatic speech recognition (ASR) systems work with indistinct forensic-like audio, in comparison with good-quality audio. Since that time, there has been rapid technological advancement, with newer systems having access to extremely large language models and having their performance proclaimed as being human-like in accuracy. This study compares various ASR systems, including OpenAI’s Whisper, to continue to test how well automatic speaker recognition works with forensic-like audio. The results show that the transcription of a good-quality audio file is at ceiling for some systems, with no errors. For the poor-quality (forensic-like) audio, Whisper was the best performing system but had only 50% of the entire speech material correct. The results for the poor-quality audio were also generally variable across the systems, with differences depending on whether a .wav or .mp3 file was used and differences between earlier and later versions of the same system. Additionally, and against expectations, Whisper showed a drop in performance over a 2-month period. While more material was transcribed in the later attempt, more was also incorrect. This study concludes that forensic-like audio is not suitable for automatic analysis.","PeriodicalId":31739,"journal":{"name":"Frontiers in Communication","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139837623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-12DOI: 10.3389/fcomm.2024.1337434
Tuomo Hiippala
This article discusses annotating and querying multimodal corpora from the perspective of Peircean semiotics. Corpora have had a significant impact on empirical research in the field of linguistics and are increasingly considered essential for multimodality research as well. I argue that Peircean semiotics can be used to gain a deeper understanding of multimodal corpora and rethink the way we work with them. I demonstrate the proposed approach in an empirical study, which uses Peircean semiotics to guide the process of querying multimodal corpora using computer vision and vector-based information retrieval. The results show that computer vision algorithms are restricted to particular domains of experience, which may be circumscribed using Peirce's theory of semiotics. However, the applicability of such algorithms may be extended using annotations, which capture aspects of meaning-making that remain beyond algorithms. Overall, the results suggest that the process of building and analysing multimodal corpora should be actively theorized in order to identify new ways of working with the information stored in them, particularly in terms of dividing the annotation tasks between humans and algorithms.
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Pub Date : 2024-02-12DOI: 10.3389/fcomm.2024.1337434
Tuomo Hiippala
This article discusses annotating and querying multimodal corpora from the perspective of Peircean semiotics. Corpora have had a significant impact on empirical research in the field of linguistics and are increasingly considered essential for multimodality research as well. I argue that Peircean semiotics can be used to gain a deeper understanding of multimodal corpora and rethink the way we work with them. I demonstrate the proposed approach in an empirical study, which uses Peircean semiotics to guide the process of querying multimodal corpora using computer vision and vector-based information retrieval. The results show that computer vision algorithms are restricted to particular domains of experience, which may be circumscribed using Peirce's theory of semiotics. However, the applicability of such algorithms may be extended using annotations, which capture aspects of meaning-making that remain beyond algorithms. Overall, the results suggest that the process of building and analysing multimodal corpora should be actively theorized in order to identify new ways of working with the information stored in them, particularly in terms of dividing the annotation tasks between humans and algorithms.
{"title":"Rethinking multimodal corpora from the perspective of Peircean semiotics","authors":"Tuomo Hiippala","doi":"10.3389/fcomm.2024.1337434","DOIUrl":"https://doi.org/10.3389/fcomm.2024.1337434","url":null,"abstract":"This article discusses annotating and querying multimodal corpora from the perspective of Peircean semiotics. Corpora have had a significant impact on empirical research in the field of linguistics and are increasingly considered essential for multimodality research as well. I argue that Peircean semiotics can be used to gain a deeper understanding of multimodal corpora and rethink the way we work with them. I demonstrate the proposed approach in an empirical study, which uses Peircean semiotics to guide the process of querying multimodal corpora using computer vision and vector-based information retrieval. The results show that computer vision algorithms are restricted to particular domains of experience, which may be circumscribed using Peirce's theory of semiotics. However, the applicability of such algorithms may be extended using annotations, which capture aspects of meaning-making that remain beyond algorithms. Overall, the results suggest that the process of building and analysing multimodal corpora should be actively theorized in order to identify new ways of working with the information stored in them, particularly in terms of dividing the annotation tasks between humans and algorithms.","PeriodicalId":31739,"journal":{"name":"Frontiers in Communication","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139844271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-12DOI: 10.3389/fcomm.2023.1234987
Jakub Mlynář, Adrien Depeursinge, John O. Prior, Roger Schaer, Alexandre Martroye de Joly, Florian Evéquoz
Technologies based on “artificial intelligence” (AI) are transforming every part of our society, including healthcare and medical institutions. An example of this trend is the novel field in oncology and radiology called radiomics, which is the extracting and mining of large-scale quantitative features from medical imaging by machine-learning (ML) algorithms. This paper explores situated work with a radiomics software platform, QuantImage (v2), and interaction around it, in educationally framed hands-on trial sessions where pairs of novice users (physicians and medical radiology technicians) work on a radiomics task consisting of developing a predictive ML model with a co-present tutor. Informed by ethnomethodology and conversation analysis (EM/CA), the results show that learning about radiomics more generally and learning how to use this platform specifically are deeply intertwined. Common-sense knowledge (e.g., about meanings of colors) can interfere with the visual representation standards established in the professional domain. Participants' skills in using the platform and knowledge of radiomics are routinely displayed in the assessment of performance measures of the resulting ML models, in the monitoring of the platform's pace of operation for possible problems, and in the ascribing of independent actions (e.g., related to algorithms) to the platform. The findings are relevant to current discussions about the explainability of AI in medicine as well as issues of machinic agency.
基于 "人工智能"（AI）的技术正在改变我们社会的方方面面，包括医疗保健和医疗机构。这一趋势的一个例子是肿瘤学和放射学中被称为放射组学的新领域，即通过机器学习（ML）算法从医学影像中提取和挖掘大规模定量特征。本文探讨了放射组学软件平台 QuantImage (v2) 的情景式工作以及与之相关的互动，在教育框架下的实践试验环节中，一对新手用户（医生和放射医学技术人员）与共同在场的导师一起完成放射组学任务，包括开发一个预测性 ML 模型。在人种方法学和会话分析（EM/CA）的启发下，研究结果表明，学习放射组学的一般知识和学习如何具体使用该平台是紧密相连的。常识性知识（如颜色的含义）可能会干扰专业领域所建立的可视化表示标准。参与者使用该平台的技能和放射组学知识通常体现在对所生成的 ML 模型的性能指标进行评估、监测平台的运行速度以发现可能存在的问题，以及将独立的操作（如与算法相关的操作）赋予平台。这些发现与当前关于人工智能在医学中的可解释性以及机器代理问题的讨论相关。
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Pub Date : 2024-02-12DOI: 10.3389/fcomm.2023.1234987
Jakub Mlynář, Adrien Depeursinge, John O. Prior, Roger Schaer, Alexandre Martroye de Joly, Florian Evéquoz
Technologies based on “artificial intelligence” (AI) are transforming every part of our society, including healthcare and medical institutions. An example of this trend is the novel field in oncology and radiology called radiomics, which is the extracting and mining of large-scale quantitative features from medical imaging by machine-learning (ML) algorithms. This paper explores situated work with a radiomics software platform, QuantImage (v2), and interaction around it, in educationally framed hands-on trial sessions where pairs of novice users (physicians and medical radiology technicians) work on a radiomics task consisting of developing a predictive ML model with a co-present tutor. Informed by ethnomethodology and conversation analysis (EM/CA), the results show that learning about radiomics more generally and learning how to use this platform specifically are deeply intertwined. Common-sense knowledge (e.g., about meanings of colors) can interfere with the visual representation standards established in the professional domain. Participants' skills in using the platform and knowledge of radiomics are routinely displayed in the assessment of performance measures of the resulting ML models, in the monitoring of the platform's pace of operation for possible problems, and in the ascribing of independent actions (e.g., related to algorithms) to the platform. The findings are relevant to current discussions about the explainability of AI in medicine as well as issues of machinic agency.
基于 "人工智能"（AI）的技术正在改变我们社会的方方面面，包括医疗保健和医疗机构。这一趋势的一个例子是肿瘤学和放射学中被称为放射组学的新领域，即通过机器学习（ML）算法从医学影像中提取和挖掘大规模定量特征。本文探讨了放射组学软件平台 QuantImage (v2) 的情景式工作以及与之相关的互动，在教育框架下的实践试验环节中，一对新手用户（医生和放射医学技术人员）与共同在场的导师一起完成放射组学任务，包括开发一个预测性 ML 模型。在人种方法学和会话分析（EM/CA）的启发下，研究结果表明，学习放射组学的一般知识和学习如何具体使用该平台是紧密相连的。常识性知识（如颜色的含义）可能会干扰专业领域所建立的可视化表示标准。参与者使用该平台的技能和放射组学知识通常体现在对所生成的 ML 模型的性能指标进行评估、监测平台的运行速度以发现可能存在的问题，以及将独立的操作（如与算法相关的操作）赋予平台。这些发现与当前关于人工智能在医学中的可解释性以及机器代理问题的讨论相关。
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Pub Date : 2024-02-08DOI: 10.3389/fcomm.2024.1298607
Sergio Barbosa, Juan Pablo Bermúdez
Public access to housing is a challenge for a large number of societies and follows a great number of limitations. Here, we test several SMS-interventions aiming at motivating people to get information on affordable loans for housing options actually available to them. We randomly assigned 6,247 people to receive an SMS inviting them to get information about government backed housing loans for which they were already eligible. SMSs followed one of 14 possible nudges including “simple” nudges (e.g., messages personalized with the recipient's name or mentioning a social norm) and “bundled” nudges (combining personalization with another simple nudge). We observed SMS response rates (i.e., responding to sign up for receiving more information) according to which nudge was assigned. No other independent variable was considered. While most nudges were more effective than a control SMS, we observed significant variation across nudges on their ability to elicit responses from users. Combinations of multiple nudges were more successful in behavior elicitation than simple nudges. We discuss the possible implications of “single” or “bundled” SMS on response rates and as an effective tool of behavior change.
{"title":"The whole is larger than the sum of its parts: additive effects of SMS nudge bundles","authors":"Sergio Barbosa, Juan Pablo Bermúdez","doi":"10.3389/fcomm.2024.1298607","DOIUrl":"https://doi.org/10.3389/fcomm.2024.1298607","url":null,"abstract":"Public access to housing is a challenge for a large number of societies and follows a great number of limitations. Here, we test several SMS-interventions aiming at motivating people to get information on affordable loans for housing options actually available to them. We randomly assigned 6,247 people to receive an SMS inviting them to get information about government backed housing loans for which they were already eligible. SMSs followed one of 14 possible nudges including “simple” nudges (e.g., messages personalized with the recipient's name or mentioning a social norm) and “bundled” nudges (combining personalization with another simple nudge). We observed SMS response rates (i.e., responding to sign up for receiving more information) according to which nudge was assigned. No other independent variable was considered. While most nudges were more effective than a control SMS, we observed significant variation across nudges on their ability to elicit responses from users. Combinations of multiple nudges were more successful in behavior elicitation than simple nudges. We discuss the possible implications of “single” or “bundled” SMS on response rates and as an effective tool of behavior change.","PeriodicalId":31739,"journal":{"name":"Frontiers in Communication","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139791016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.3389/fcomm.2024.1316677
Deborah D. Sellnow-Richmond, Marta Natalia Lukacovic, Scott Sellnow-Richmond
Exemplification, the use of emotionally evocative messages to elicit a response based on impression formation, are frequently present in news messages. The present study examined the use of positive vs. negative exemplars in news stories to determine the role of stigmatization and securitization in these messages and whether this impacts perceptions of the importance and quality of news. This study tested exemplification's effects using three conditions: positive, negative, and non-exemplar news stories—two valences of exemplification and a control condition. Results indicate that as stigmatized impressions increase, securitization decreases, valence of exemplification predicts perceptions on the quality of news, and valence of exemplification predicts perceptions on the general interest of the issues. Implications suggest news message creators should consider positive exemplars in place of negative exemplars to minimize unintended negative effects.
{"title":"Exemplification in news narratives: stigmatizing and securitizing effects","authors":"Deborah D. Sellnow-Richmond, Marta Natalia Lukacovic, Scott Sellnow-Richmond","doi":"10.3389/fcomm.2024.1316677","DOIUrl":"https://doi.org/10.3389/fcomm.2024.1316677","url":null,"abstract":"Exemplification, the use of emotionally evocative messages to elicit a response based on impression formation, are frequently present in news messages. The present study examined the use of positive vs. negative exemplars in news stories to determine the role of stigmatization and securitization in these messages and whether this impacts perceptions of the importance and quality of news. This study tested exemplification's effects using three conditions: positive, negative, and non-exemplar news stories—two valences of exemplification and a control condition. Results indicate that as stigmatized impressions increase, securitization decreases, valence of exemplification predicts perceptions on the quality of news, and valence of exemplification predicts perceptions on the general interest of the issues. Implications suggest news message creators should consider positive exemplars in place of negative exemplars to minimize unintended negative effects.","PeriodicalId":31739,"journal":{"name":"Frontiers in Communication","volume":null,"pages":null},"PeriodicalIF":2.4,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139852405","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-08DOI: 10.3389/fcomm.2024.1316677
Deborah D. Sellnow-Richmond, Marta Natalia Lukacovic, Scott Sellnow-Richmond
Exemplification, the use of emotionally evocative messages to elicit a response based on impression formation, are frequently present in news messages. The present study examined the use of positive vs. negative exemplars in news stories to determine the role of stigmatization and securitization in these messages and whether this impacts perceptions of the importance and quality of news. This study tested exemplification's effects using three conditions: positive, negative, and non-exemplar news stories—two valences of exemplification and a control condition. Results indicate that as stigmatized impressions increase, securitization decreases, valence of exemplification predicts perceptions on the quality of news, and valence of exemplification predicts perceptions on the general interest of the issues. Implications suggest news message creators should consider positive exemplars in place of negative exemplars to minimize unintended negative effects.
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Pub Date : 2024-02-08DOI: 10.3389/fcomm.2024.1298607
Sergio Barbosa, Juan Pablo Bermúdez
Public access to housing is a challenge for a large number of societies and follows a great number of limitations. Here, we test several SMS-interventions aiming at motivating people to get information on affordable loans for housing options actually available to them. We randomly assigned 6,247 people to receive an SMS inviting them to get information about government backed housing loans for which they were already eligible. SMSs followed one of 14 possible nudges including “simple” nudges (e.g., messages personalized with the recipient's name or mentioning a social norm) and “bundled” nudges (combining personalization with another simple nudge). We observed SMS response rates (i.e., responding to sign up for receiving more information) according to which nudge was assigned. No other independent variable was considered. While most nudges were more effective than a control SMS, we observed significant variation across nudges on their ability to elicit responses from users. Combinations of multiple nudges were more successful in behavior elicitation than simple nudges. We discuss the possible implications of “single” or “bundled” SMS on response rates and as an effective tool of behavior change.
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Pub Date : 2024-02-02DOI: 10.3389/fcomm.2024.1292961
Kealeboga Aiseng
Bereavement is something that we experience in one way or another. It involves many steps from one culture to the other. Many scholars have documented the role of social media tools in bereavement processes. In this study, I look at the challenges and opportunities offered by Facebook during bereavement, especially in a community that is still traditional and has yet to fully comprehend the importance of social media, particularly in matters considered sacred. The study used interviews with residents from Taung to collect data. Thematic analysis was used to analyze the collected data from the interviews. The study findings indicate some challenges associated with bereavement on Facebook: emotional shock, lack of sensitivity, misinformation, and cultural dilution. There are also opportunities: fast news sharing, ongoing emotional support, and sharing of memories. The study argues that the findings should expand our understanding and knowledge of bereavement in some African cultures and use social media tools to complement and not destroy African beliefs and practices.
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