Pub Date : 2024-10-16DOI: 10.1177/08944393241282602
Jack Black
This article poses a simple question: can AI lie? In response to this question, the article examines, as its point of inquiry, popular AI chatbots, such as, ChatGPT. In doing so, an examination of the psychoanalytic, philosophical, and technological significance of AI and its complexities are located in relation to the dynamics of truth, falsity, and deception. That is, by critically considering the chatbot’s ability to engage in natural language conversations and provide contextually relevant responses, it is argued that what separates the AI chatbot from anthropocentric debates, which allude to some form of conscious recognition on behalf of AI, is the importance of the lie – an importance which a psychoanalytic approach can reveal. Indeed, while AI technologies can undoubtedly blur the line between lies and truth-speaking, in the case of the AI chatbot, it is detailed how such technology remains unable to lie authentically or, in other words, is unable to lie like a human. For psychoanalysis, the capacity to lie bears witness to the unconscious and, thus, plays an important role in determining the subject. It is for this reason that rather than uncritically accepting the chatbot’s authority – an authority that is easily reflected in its honest responses and frank admissions – a psychoanalytic (Lacanian) perspective can highlight the significance of the unconscious as a distorting factor in determining the subject. To help elucidate this argument, specific attention is given to introducing and applying Lacan’s subject of enunciation and subject of the enunciated. This is used to assert that what continues (for now) to set us apart from AI technology is not necessarily our ‘better knowledge’ but our capability to consciously engage in acts of falsehood that function to reveal the social nuances and significances of the lie.
{"title":"Can AI Lie? Chabot Technologies, the Subject, and the Importance of Lying","authors":"Jack Black","doi":"10.1177/08944393241282602","DOIUrl":"https://doi.org/10.1177/08944393241282602","url":null,"abstract":"This article poses a simple question: can AI lie? In response to this question, the article examines, as its point of inquiry, popular AI chatbots, such as, ChatGPT. In doing so, an examination of the psychoanalytic, philosophical, and technological significance of AI and its complexities are located in relation to the dynamics of truth, falsity, and deception. That is, by critically considering the chatbot’s ability to engage in natural language conversations and provide contextually relevant responses, it is argued that what separates the AI chatbot from anthropocentric debates, which allude to some form of conscious recognition on behalf of AI, is the importance of the lie – an importance which a psychoanalytic approach can reveal. Indeed, while AI technologies can undoubtedly blur the line between lies and truth-speaking, in the case of the AI chatbot, it is detailed how such technology remains unable to lie authentically or, in other words, is unable to lie like a human. For psychoanalysis, the capacity to lie bears witness to the unconscious and, thus, plays an important role in determining the subject. It is for this reason that rather than uncritically accepting the chatbot’s authority – an authority that is easily reflected in its honest responses and frank admissions – a psychoanalytic (Lacanian) perspective can highlight the significance of the unconscious as a distorting factor in determining the subject. To help elucidate this argument, specific attention is given to introducing and applying Lacan’s subject of enunciation and subject of the enunciated. This is used to assert that what continues (for now) to set us apart from AI technology is not necessarily our ‘better knowledge’ but our capability to consciously engage in acts of falsehood that function to reveal the social nuances and significances of the lie.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142444488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-16DOI: 10.1177/08944393241287793
Silke Adam, Mykola Makhortykh, Michaela Maier, Viktor Aigenseer, Aleksandra Urman, Teresa Gil Lopez, Clara Christner, Ernesto de León, Roberto Ulloa
This article evaluates the quality of data collection in individual-level desktop web tracking used in the social sciences and shows that the existing approaches face sampling issues, validity issues due to the lack of content-level data and their disregard for the variety of devices and long-tail consumption patterns as well as transparency and privacy issues. To overcome some of these problems, the article introduces a new academic web tracking solution, WebTrack, an open-source tracking tool maintained by a major European research institution, GESIS. The design logic, the interfaces, and the backend requirements for WebTrack are discussed, followed by a detailed examination of the strengths and weaknesses of the tool. Finally, using data from 1,185 participants, the article empirically illustrates how an improvement in data collection through WebTrack leads to innovative shifts in the use of tracking data. As WebTrack allows for collecting the content people are exposed to beyond the classical news platforms, it can greatly improve the detection of politics-related information consumption in tracking data through automated content analysis compared to traditional approaches that rely on the source-level analysis.
{"title":"Improving the Quality of Individual-Level Web Tracking: Challenges of Existing Approaches and Introduction of a New Content and Long-Tail Sensitive Academic Solution","authors":"Silke Adam, Mykola Makhortykh, Michaela Maier, Viktor Aigenseer, Aleksandra Urman, Teresa Gil Lopez, Clara Christner, Ernesto de León, Roberto Ulloa","doi":"10.1177/08944393241287793","DOIUrl":"https://doi.org/10.1177/08944393241287793","url":null,"abstract":"This article evaluates the quality of data collection in individual-level desktop web tracking used in the social sciences and shows that the existing approaches face sampling issues, validity issues due to the lack of content-level data and their disregard for the variety of devices and long-tail consumption patterns as well as transparency and privacy issues. To overcome some of these problems, the article introduces a new academic web tracking solution, WebTrack, an open-source tracking tool maintained by a major European research institution, GESIS. The design logic, the interfaces, and the backend requirements for WebTrack are discussed, followed by a detailed examination of the strengths and weaknesses of the tool. Finally, using data from 1,185 participants, the article empirically illustrates how an improvement in data collection through WebTrack leads to innovative shifts in the use of tracking data. As WebTrack allows for collecting the content people are exposed to beyond the classical news platforms, it can greatly improve the detection of politics-related information consumption in tracking data through automated content analysis compared to traditional approaches that rely on the source-level analysis.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142444489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-10-12DOI: 10.1177/08944393241279421
Tobias Gummer, Anne-Sophie Oehrlein
Google Trends (GT) data are increasingly used in the social sciences and adjacent fields. However, previous research on the quality of GT data has raised concerns regarding their reliability. In the present study, we investigated whether reliability differs between low- and high-frequency search terms. In other words, we explored the existence of a reliability-frequency continuum in GT data. Our study adds to previous research by investigating a more comprehensive set of search terms and different aspects of reliability (e.g., differences in relative search volume distributions, correctly identified maxima). For this purpose, we collected samples of GT data for ten high- and two low-frequency search terms. We obtained one real-time sample and 62 non–realtime samples per search term (30 non–realtime samples for low-frequency search terms). Data collection was restricted to search data for Germany. Our data support the existence of a reliability-frequency continuum—low-frequency search terms are subject to greater reliability issues compared to high-frequency search terms. Based on our findings, we have derived practical recommendations for the use of GT data and have outlined future research opportunities.
{"title":"Using Google Trends Data to Study High-Frequency Search Terms: Evidence for a Reliability-Frequency Continuum","authors":"Tobias Gummer, Anne-Sophie Oehrlein","doi":"10.1177/08944393241279421","DOIUrl":"https://doi.org/10.1177/08944393241279421","url":null,"abstract":"Google Trends (GT) data are increasingly used in the social sciences and adjacent fields. However, previous research on the quality of GT data has raised concerns regarding their reliability. In the present study, we investigated whether reliability differs between low- and high-frequency search terms. In other words, we explored the existence of a reliability-frequency continuum in GT data. Our study adds to previous research by investigating a more comprehensive set of search terms and different aspects of reliability (e.g., differences in relative search volume distributions, correctly identified maxima). For this purpose, we collected samples of GT data for ten high- and two low-frequency search terms. We obtained one real-time sample and 62 non–realtime samples per search term (30 non–realtime samples for low-frequency search terms). Data collection was restricted to search data for Germany. Our data support the existence of a reliability-frequency continuum—low-frequency search terms are subject to greater reliability issues compared to high-frequency search terms. Based on our findings, we have derived practical recommendations for the use of GT data and have outlined future research opportunities.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142430476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-23DOI: 10.1177/08944393241286471
Petter Törnberg
Instruction-tuned Large Language Models (LLMs) have recently emerged as a powerful new tool for text analysis. As these models are capable of zero-shot annotation based on instructions written in natural language, they obviate the need of large sets of training data—and thus bring potential paradigm-shifting implications for using text as data. While the models show substantial promise, their relative performance compared to human coders and supervised models remains poorly understood and subject to significant academic debate. This paper assesses the strengths and weaknesses of popular fine-tuned AI models compared to both conventional supervised classifiers and manual annotation by experts and crowd workers. The task used is to identify the political affiliation of politicians based on a single X/Twitter message, focusing on data from 11 different countries. The paper finds that GPT-4 achieves higher accuracy than both supervised models and human coders across all languages and country contexts. In the US context, it achieves an accuracy of 0.934 and an inter-coder reliability of 0.982. Examining the cases where the models fail, the paper finds that the LLM—unlike the supervised models—correctly annotates messages that require interpretation of implicit or unspoken references, or reasoning on the basis of contextual knowledge—capacities that have traditionally been understood to be distinctly human. The paper thus contributes to our understanding of the revolutionary implications of LLMs for text analysis within the social sciences.
{"title":"Large Language Models Outperform Expert Coders and Supervised Classifiers at Annotating Political Social Media Messages","authors":"Petter Törnberg","doi":"10.1177/08944393241286471","DOIUrl":"https://doi.org/10.1177/08944393241286471","url":null,"abstract":"Instruction-tuned Large Language Models (LLMs) have recently emerged as a powerful new tool for text analysis. As these models are capable of zero-shot annotation based on instructions written in natural language, they obviate the need of large sets of training data—and thus bring potential paradigm-shifting implications for using text as data. While the models show substantial promise, their relative performance compared to human coders and supervised models remains poorly understood and subject to significant academic debate. This paper assesses the strengths and weaknesses of popular fine-tuned AI models compared to both conventional supervised classifiers and manual annotation by experts and crowd workers. The task used is to identify the political affiliation of politicians based on a single X/Twitter message, focusing on data from 11 different countries. The paper finds that GPT-4 achieves higher accuracy than both supervised models and human coders across all languages and country contexts. In the US context, it achieves an accuracy of 0.934 and an inter-coder reliability of 0.982. Examining the cases where the models fail, the paper finds that the LLM—unlike the supervised models—correctly annotates messages that require interpretation of implicit or unspoken references, or reasoning on the basis of contextual knowledge—capacities that have traditionally been understood to be distinctly human. The paper thus contributes to our understanding of the revolutionary implications of LLMs for text analysis within the social sciences.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142313736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-21DOI: 10.1177/08944393241286339
Filippo Andrei, Giuseppe Alessandro Veltri
Information technologies have transformed many aspects of social life, including how illegal goods are exchanged. Illegal online markets are now flourishing on various channels: the surface web (all websites accessible through a standard browser), the dark web (an encrypted internet network only accessible via anonymous browsers), and encrypted messaging applications installed on smartphones. These marketplaces take many forms, including simple web shops, chat rooms, forums, social media marketplaces, and platforms. This study focuses on the largest known darknet platform to date: AlphaBay. This cryptomarket operated from December 2014 until July 2017, when an international police operation shut it down. The dataset contains 6033 vendor profiles collected in January 2017. Using three generalized additive models (GAMs), we show that seller status positively affects sales, revenue, and sales through finalized early payment. Once sellers gain status on the platforms, they make more sales without a semi-institutionalized form of payment (e.g. escrow). On the other hand, buyers relying on status metrics as cognitive shortcuts tend to choose vendors even if they do not offer payment protection tools.
{"title":"Status Spill-Over in Cryptomarket for Illegal Goods","authors":"Filippo Andrei, Giuseppe Alessandro Veltri","doi":"10.1177/08944393241286339","DOIUrl":"https://doi.org/10.1177/08944393241286339","url":null,"abstract":"Information technologies have transformed many aspects of social life, including how illegal goods are exchanged. Illegal online markets are now flourishing on various channels: the surface web (all websites accessible through a standard browser), the dark web (an encrypted internet network only accessible via anonymous browsers), and encrypted messaging applications installed on smartphones. These marketplaces take many forms, including simple web shops, chat rooms, forums, social media marketplaces, and platforms. This study focuses on the largest known darknet platform to date: AlphaBay. This cryptomarket operated from December 2014 until July 2017, when an international police operation shut it down. The dataset contains 6033 vendor profiles collected in January 2017. Using three generalized additive models (GAMs), we show that seller status positively affects sales, revenue, and sales through finalized early payment. Once sellers gain status on the platforms, they make more sales without a semi-institutionalized form of payment (e.g. escrow). On the other hand, buyers relying on status metrics as cognitive shortcuts tend to choose vendors even if they do not offer payment protection tools.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142306251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-20DOI: 10.1177/08944393241286149
Zahedur Rahman Arman
This study undertook an analysis of network agenda setting during the 2020 U.S. Presidential campaign, focusing on the interactions between the campaigns and their respective supporters within the context of a polarized social media environment. By employing social network analysis techniques to examine issue agendas, the study revealed a relatively weak correlation between the agendas of the campaigns and their affiliated supporters on Facebook. Conversely, it found a notable association between entities sharing the same ideological orientation—party supporters displayed a higher degree of engagement with their own party’s campaign, and vice versa. The implications of these findings, from a theoretical, methodological, and practical standpoint, have been thoroughly discussed.
{"title":"Network Issue Agenda Setting on Facebook: Exploring the Interplay Between Polarized Campaigns and Party Supporters","authors":"Zahedur Rahman Arman","doi":"10.1177/08944393241286149","DOIUrl":"https://doi.org/10.1177/08944393241286149","url":null,"abstract":"This study undertook an analysis of network agenda setting during the 2020 U.S. Presidential campaign, focusing on the interactions between the campaigns and their respective supporters within the context of a polarized social media environment. By employing social network analysis techniques to examine issue agendas, the study revealed a relatively weak correlation between the agendas of the campaigns and their affiliated supporters on Facebook. Conversely, it found a notable association between entities sharing the same ideological orientation—party supporters displayed a higher degree of engagement with their own party’s campaign, and vice versa. The implications of these findings, from a theoretical, methodological, and practical standpoint, have been thoroughly discussed.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142306281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-10DOI: 10.1177/08944393241275641
Ali Unlu, Sophie Truong, Nitin Sawhney, Tuukka Tammi
This article presents the results of a comprehensive study examining the influence of bots on the dissemination of COVID-19 misinformation and negative vaccine stance on Twitter over a period of three years. The research employed a tripartite methodology: text classification, topic modeling, and network analysis to explore this phenomenon. Text classification, leveraging the Turku University FinBERT pre-trained embeddings model, differentiated between misinformation and vaccine stance detection. Bot-like Twitter accounts were identified using the Botometer software, and further analysis was implemented to distinguish COVID-19 specific bot accounts from regular bots. Network analysis illuminated the communication patterns of COVID-19 bots within retweet and mention networks. The findings reveal that these bots exhibit distinct characteristics and tactics that enable them to influence public discourse, particularly showing an increased activity in COVID-19-related conversations. Topic modeling analysis uncovers that COVID-19 bots predominantly focused on themes such as safety, political/conspiracy theories, and personal choice. The study highlights the critical need to develop effective strategies for detecting and countering bot influence. Essential actions include using clear and concise language in health communications, establishing strategic partnerships during crises, and ensuring the authenticity of user accounts on digital platforms. The findings underscore the pivotal role of bots in propagating misinformation related to COVID-19 and vaccines, highlighting the necessity of identifying and mitigating bot activities for effective intervention.
{"title":"Unveiling the Veiled Threat: The Impact of Bots on COVID-19 Health Communication","authors":"Ali Unlu, Sophie Truong, Nitin Sawhney, Tuukka Tammi","doi":"10.1177/08944393241275641","DOIUrl":"https://doi.org/10.1177/08944393241275641","url":null,"abstract":"This article presents the results of a comprehensive study examining the influence of bots on the dissemination of COVID-19 misinformation and negative vaccine stance on Twitter over a period of three years. The research employed a tripartite methodology: text classification, topic modeling, and network analysis to explore this phenomenon. Text classification, leveraging the Turku University FinBERT pre-trained embeddings model, differentiated between misinformation and vaccine stance detection. Bot-like Twitter accounts were identified using the Botometer software, and further analysis was implemented to distinguish COVID-19 specific bot accounts from regular bots. Network analysis illuminated the communication patterns of COVID-19 bots within retweet and mention networks. The findings reveal that these bots exhibit distinct characteristics and tactics that enable them to influence public discourse, particularly showing an increased activity in COVID-19-related conversations. Topic modeling analysis uncovers that COVID-19 bots predominantly focused on themes such as safety, political/conspiracy theories, and personal choice. The study highlights the critical need to develop effective strategies for detecting and countering bot influence. Essential actions include using clear and concise language in health communications, establishing strategic partnerships during crises, and ensuring the authenticity of user accounts on digital platforms. The findings underscore the pivotal role of bots in propagating misinformation related to COVID-19 and vaccines, highlighting the necessity of identifying and mitigating bot activities for effective intervention.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142166075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-04DOI: 10.1177/08944393241279418
Nils Brandenstein, Christian Montag, Cornelia Sindermann
Studying political opinions of citizens stands as a fundamental pursuit for both policymakers and researchers. While traditional surveys remain the primary method to investigate individual political opinions, the advent of social media data (SMD) offers novel prospects. However, the number of studies using SMD to extract individuals’ political opinions are limited and differ greatly in their methodological approaches and levels of success. Recent studies highlight the benefits of analyzing individuals’ social media network structure to estimate political opinions. Nevertheless, current methodologies exhibit limitations, including the use of simplistic linear models and a predominant focus on samples from the United States. Addressing these issues, we employ an unsupervised Variational Autoencoder (VAE) machine learning model to extract individual opinion estimates from SMD of N = 276 008 German Twitter (now called ’X’) users, compare its performance to a linear model and validate model estimates on self-reported opinion measures. Our findings suggest that the VAE captures Twitter users’ network structure more precisely, leading to higher accuracy in following decision predictions and associations with self-reported political ideology and voting intentions. Our study emphasizes the need for advanced analytical approaches capable to capture complex relationships in social media networks when studying political opinion, at least in non-US contexts.
{"title":"To Follow or Not to Follow: Estimating Political Opinion From Twitter Data Using a Network-Based Machine Learning Approach","authors":"Nils Brandenstein, Christian Montag, Cornelia Sindermann","doi":"10.1177/08944393241279418","DOIUrl":"https://doi.org/10.1177/08944393241279418","url":null,"abstract":"Studying political opinions of citizens stands as a fundamental pursuit for both policymakers and researchers. While traditional surveys remain the primary method to investigate individual political opinions, the advent of social media data (SMD) offers novel prospects. However, the number of studies using SMD to extract individuals’ political opinions are limited and differ greatly in their methodological approaches and levels of success. Recent studies highlight the benefits of analyzing individuals’ social media network structure to estimate political opinions. Nevertheless, current methodologies exhibit limitations, including the use of simplistic linear models and a predominant focus on samples from the United States. Addressing these issues, we employ an unsupervised Variational Autoencoder (VAE) machine learning model to extract individual opinion estimates from SMD of N = 276 008 German Twitter (now called ’X’) users, compare its performance to a linear model and validate model estimates on self-reported opinion measures. Our findings suggest that the VAE captures Twitter users’ network structure more precisely, leading to higher accuracy in following decision predictions and associations with self-reported political ideology and voting intentions. Our study emphasizes the need for advanced analytical approaches capable to capture complex relationships in social media networks when studying political opinion, at least in non-US contexts.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142142553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-02DOI: 10.1177/08944393241268526
Mikyung Chang
This study aims to analyze news coverage on artificial intelligence (AI) issues and highlight the characteristics and differences in reporting based on media partisanship. By examining AI-related news in the South Korean media, this study reveals how conservative and progressive outlets frame the issue differently. The analysis found that conservative media coverage predominantly focuses on positive aspects, emphasizing development value frames such as the benefits and societal progress brought by AI. In contrast, progressive media often highlight crisis value frames, focusing on issues like side effects, ethical concerns, and legislation surrounding AI. These partisan differences reflect fundamental societal priorities and influence public discourse and policy agendas. Understanding media framing is crucial for fostering informed public dialogue on the societal significance of AI and promoting evidence-based decision-making. By recognizing partisan biases and critically evaluating media coverage, citizens can engage in constructive discourse beyond ideological divides. This study underscores the role of the media in promoting interdisciplinary discussions about the future trajectory of AI and in preparing society for its impacts. Ultimately, evidence-based public discourse is essential for shaping responsible AI policies and mitigating potential risks in the digital age.
{"title":"Does the Media’s Partisanship Influence News Coverage on Artificial Intelligence Issues? Media Coverage Analysis on Artificial Intelligence Issues","authors":"Mikyung Chang","doi":"10.1177/08944393241268526","DOIUrl":"https://doi.org/10.1177/08944393241268526","url":null,"abstract":"This study aims to analyze news coverage on artificial intelligence (AI) issues and highlight the characteristics and differences in reporting based on media partisanship. By examining AI-related news in the South Korean media, this study reveals how conservative and progressive outlets frame the issue differently. The analysis found that conservative media coverage predominantly focuses on positive aspects, emphasizing development value frames such as the benefits and societal progress brought by AI. In contrast, progressive media often highlight crisis value frames, focusing on issues like side effects, ethical concerns, and legislation surrounding AI. These partisan differences reflect fundamental societal priorities and influence public discourse and policy agendas. Understanding media framing is crucial for fostering informed public dialogue on the societal significance of AI and promoting evidence-based decision-making. By recognizing partisan biases and critically evaluating media coverage, citizens can engage in constructive discourse beyond ideological divides. This study underscores the role of the media in promoting interdisciplinary discussions about the future trajectory of AI and in preparing society for its impacts. Ultimately, evidence-based public discourse is essential for shaping responsible AI policies and mitigating potential risks in the digital age.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142123492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"社会学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-29DOI: 10.1177/08944393241279422
Meredith E. David, James A. Roberts
Phubbing (phone snubbing) has become the norm in (im)polite society. A vast majority of US adults report using their phones during a recent social interaction. Using one’s phone in the presence of others has been shown to have a negative impact on relationships among co-workers, friends, family, and romantic partners. Recent research suggests viewing short-form videos (SFVs) (e.g., TikTok) is more addictive/immersive than traditional social media (e.g., Facebook) leading to a greater likelihood of phubbing others. Across two studies, the present research investigates the relationship between SFV viewing and phubbing and the possible mediating effect of self-control. We also test whether TikTok has a stronger relationship with phubbing than Instagram Reels and YouTube Shorts, two popular SFV purveyors. Study 1 (282 college students) finds that viewing TikTok videos is positively associated with phubbing others and this relationship is mediated by self-control. Interestingly, Study 1 also finds that this relationship does not hold for Instagram Reels and YouTube shorts. Using two different measures of self-control, Study 2 (198 adults) provides additional support for the mediating effect of self-control on the SFV viewing—phubbing relationship. Again, the model is only supported for TikTok SFV viewing, not Instagram or YouTube. In sum, the viewing of carefully curated short TikTok videos, often 30–60 seconds in length, undermines self-control which is associated with increased phubbing behavior. Implications of the present study’s findings expand far beyond phubbing. Self-control plays a central role in nearly all human decision making and behavior. Suggestions for future research are offered.
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