Pub Date : 2024-05-21DOI: 10.1177/08944393241252640
Tien Ee Dominic Yeo, Tsz Hang Chu
Hashtag campaigns calling out sexual violence and rape myths offer a unique context for disclosing sexual victimization on social media. This study investigates the applicability of adaptive self-reflection as a potential self-effect from such public disclosures of unreported sexual victimization experiences by analyzing 92,583 tweets that invoked #WhyIDidntReport. A supervised machine learning classifier determined that 61.8% of the tweets were self-disclosures of sexual victimization. Linguistic Inquiry and Word Count (LIWC) analysis showed statistically significant differences in four psycholinguistic dimensions (greater use of past focus, cognitive processes, insight, and causation words) connected with reflective processing in tweets with self-disclosed sexual victimization compared to those without. Additionally, topic modeling and thematic analysis identified nine salient topics within the self-disclosing tweets, comprising three self-distanced representations (i.e., relatively abstract and insightful construals) of the unwanted experiences: (a) acknowledging one’s previously unacknowledged victimization, (b) reaffirming one’s rationale for not reporting, and (c) decrying invalidating response to one’s disclosure. Moving beyond reception effects and social support in extant research about social media as a coping tool, this study provides new empirical insights into the potential of social media to promote expressive meaning-making of upsetting and traumatic experiences in ways that support recovery and resilience.
{"title":"Adaptive Self-Reflection as a Social Media Self-Effect: Insights from Computational Text Analyses of Self-Disclosures of Unreported Sexual Victimization in a Hashtag Campaign","authors":"Tien Ee Dominic Yeo, Tsz Hang Chu","doi":"10.1177/08944393241252640","DOIUrl":"https://doi.org/10.1177/08944393241252640","url":null,"abstract":"Hashtag campaigns calling out sexual violence and rape myths offer a unique context for disclosing sexual victimization on social media. This study investigates the applicability of adaptive self-reflection as a potential self-effect from such public disclosures of unreported sexual victimization experiences by analyzing 92,583 tweets that invoked #WhyIDidntReport. A supervised machine learning classifier determined that 61.8% of the tweets were self-disclosures of sexual victimization. Linguistic Inquiry and Word Count (LIWC) analysis showed statistically significant differences in four psycholinguistic dimensions (greater use of past focus, cognitive processes, insight, and causation words) connected with reflective processing in tweets with self-disclosed sexual victimization compared to those without. Additionally, topic modeling and thematic analysis identified nine salient topics within the self-disclosing tweets, comprising three self-distanced representations (i.e., relatively abstract and insightful construals) of the unwanted experiences: (a) acknowledging one’s previously unacknowledged victimization, (b) reaffirming one’s rationale for not reporting, and (c) decrying invalidating response to one’s disclosure. Moving beyond reception effects and social support in extant research about social media as a coping tool, this study provides new empirical insights into the potential of social media to promote expressive meaning-making of upsetting and traumatic experiences in ways that support recovery and resilience.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"40 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141079280","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}
In 2014, the EU introduced the lead candidate procedure to raise citizens’ awareness and interest in the European Parliament (EP) elections and, thereby, voter turnout. We study the use of personalization, centralized personalization (focusing on lead candidates), emotional personalization, and private personalization on Facebook by political parties across 12 countries during the 2014 and 2019 EP campaigns and the effects of personalization on user engagement. A standardized quantitative content analysis of 14,293 posts by 227 political parties shows that about half of the Facebook posts were personalized, but there is no general trend of rising personalization. While emotional personalization increased, parties hardly ever posted about their lead candidates and their private lives. Variations are not due to structural (e.g., party and media systems) or geographical/cultural factors. Positive effects are found for the use of emotional personalization attracting a higher volume of user reactions (likes, reactions, shares, and comments) in both elections.
{"title":"Personal, Private, Emotional? How Political Parties Use Personalization Strategies on Facebook in the 2014 and 2019 EP Election Campaigns","authors":"Uta Russmann, Ulrike Klinger, Karolina Koc-Michalska","doi":"10.1177/08944393241254807","DOIUrl":"https://doi.org/10.1177/08944393241254807","url":null,"abstract":"In 2014, the EU introduced the lead candidate procedure to raise citizens’ awareness and interest in the European Parliament (EP) elections and, thereby, voter turnout. We study the use of personalization, centralized personalization (focusing on lead candidates), emotional personalization, and private personalization on Facebook by political parties across 12 countries during the 2014 and 2019 EP campaigns and the effects of personalization on user engagement. A standardized quantitative content analysis of 14,293 posts by 227 political parties shows that about half of the Facebook posts were personalized, but there is no general trend of rising personalization. While emotional personalization increased, parties hardly ever posted about their lead candidates and their private lives. Variations are not due to structural (e.g., party and media systems) or geographical/cultural factors. Positive effects are found for the use of emotional personalization attracting a higher volume of user reactions (likes, reactions, shares, and comments) in both elections.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"25 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140961534","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 : 2024-05-14DOI: 10.1177/08944393241252638
Felix Schmidt, Sebastian Stier, Lukas Otto
Incivility, that is, the breaking of social norms of conversation, is evidently prevalent in online political communication. While a growing literature provides evidence on the prevalence of incivility in different online venues, it is still unclear where and to what extent Internet users are exposed to incivility. This paper takes a comparative approach to assess the levels of incivility across contexts, content and personal characteristics. The pre-registered analysis uses detailed web browsing histories, including public Facebook posts and tweets seen by study participants, in combination with surveys collected during the German federal election 2021 ( N = 739). The level of incivility is predicted using Google’s Perspective API and compared across contexts (platforms and campaign periods), content features, and individual-level variables. The findings show that incivility is particularly strong on Twitter and more prevalent in comments than original posts/tweets on Facebook and Twitter. Content featuring political content and actors is more uncivil, whereas personal characteristics are less relevant predictors. The finding that user-generated political content is the most likely source of individuals’ exposure to incivility adds to the understanding of social media’s impact on public discourse.
{"title":"Incivility in Comparison: How Context, Content, and Personal Characteristics Predict Exposure to Uncivil Content","authors":"Felix Schmidt, Sebastian Stier, Lukas Otto","doi":"10.1177/08944393241252638","DOIUrl":"https://doi.org/10.1177/08944393241252638","url":null,"abstract":"Incivility, that is, the breaking of social norms of conversation, is evidently prevalent in online political communication. While a growing literature provides evidence on the prevalence of incivility in different online venues, it is still unclear where and to what extent Internet users are exposed to incivility. This paper takes a comparative approach to assess the levels of incivility across contexts, content and personal characteristics. The pre-registered analysis uses detailed web browsing histories, including public Facebook posts and tweets seen by study participants, in combination with surveys collected during the German federal election 2021 ( N = 739). The level of incivility is predicted using Google’s Perspective API and compared across contexts (platforms and campaign periods), content features, and individual-level variables. The findings show that incivility is particularly strong on Twitter and more prevalent in comments than original posts/tweets on Facebook and Twitter. Content featuring political content and actors is more uncivil, whereas personal characteristics are less relevant predictors. The finding that user-generated political content is the most likely source of individuals’ exposure to incivility adds to the understanding of social media’s impact on public discourse.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"124 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140942985","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 : 2024-05-10DOI: 10.1177/08944393241249720
Kristen Cibelli Hibben, Zachary Smith, Benjamin Rogers, Valerie Ryan, Paul Scanlon, Travis Hoppe
Open-ended survey questions can enable researchers to gain insights beyond more commonly used closed-ended question formats by allowing respondents an opportunity to provide information with few constraints and in their own words. Open-ended web probes are also increasingly used to inform the design and evaluation of survey questions. However, open-ended questions are more susceptible to insufficient or irrelevant responses that can be burdensome and time-consuming to identify and remove manually, often resulting in underuse of open-ended questions and, when used, potential inclusion of poor-quality data. To address these challenges, we developed and publicly released the Semi-Automated Nonresponse Detection for Survey text (SANDS), an item nonresponse detection approach based on a Bidirectional Transformer for Language Understanding model, fine-tuned using Simple Contrastive Sentence Embedding and targeted human coding, to categorize open-ended text data as valid or likely nonresponse. This approach is powerful in that it uses natural language processing as opposed to existing nonresponse detection approaches that have relied exclusively on rules or regular expressions or used bag-of-words approaches that tend to perform less well on short pieces of text, typos, or uncommon words, often prevalent in open-text survey data. This paper presents the development of SANDS and a quantitative evaluation of its performance and potential bias using open-text responses from a series of web probes as case studies. Overall, the SANDS model performed well in identifying a dataset of likely valid results to be used for quantitative or qualitative analysis, particularly on health-related data. Developed for generalizable use and accessible to others, the SANDS model can greatly improve the efficiency of identifying inadequate and irrelevant open-text responses, offering expanded opportunities for the use of open-text data to inform question design and improve survey data quality.
{"title":"Semi-Automated Nonresponse Detection for Open-Text Survey Data","authors":"Kristen Cibelli Hibben, Zachary Smith, Benjamin Rogers, Valerie Ryan, Paul Scanlon, Travis Hoppe","doi":"10.1177/08944393241249720","DOIUrl":"https://doi.org/10.1177/08944393241249720","url":null,"abstract":"Open-ended survey questions can enable researchers to gain insights beyond more commonly used closed-ended question formats by allowing respondents an opportunity to provide information with few constraints and in their own words. Open-ended web probes are also increasingly used to inform the design and evaluation of survey questions. However, open-ended questions are more susceptible to insufficient or irrelevant responses that can be burdensome and time-consuming to identify and remove manually, often resulting in underuse of open-ended questions and, when used, potential inclusion of poor-quality data. To address these challenges, we developed and publicly released the Semi-Automated Nonresponse Detection for Survey text (SANDS), an item nonresponse detection approach based on a Bidirectional Transformer for Language Understanding model, fine-tuned using Simple Contrastive Sentence Embedding and targeted human coding, to categorize open-ended text data as valid or likely nonresponse. This approach is powerful in that it uses natural language processing as opposed to existing nonresponse detection approaches that have relied exclusively on rules or regular expressions or used bag-of-words approaches that tend to perform less well on short pieces of text, typos, or uncommon words, often prevalent in open-text survey data. This paper presents the development of SANDS and a quantitative evaluation of its performance and potential bias using open-text responses from a series of web probes as case studies. Overall, the SANDS model performed well in identifying a dataset of likely valid results to be used for quantitative or qualitative analysis, particularly on health-related data. Developed for generalizable use and accessible to others, the SANDS model can greatly improve the efficiency of identifying inadequate and irrelevant open-text responses, offering expanded opportunities for the use of open-text data to inform question design and improve survey data quality.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"12 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140910676","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 : 2024-05-07DOI: 10.1177/08944393241252027
Ke M. Huang-Isherwood, Jaeho Cho, Joo-Wha Hong, Eugene Lee
Chatbots have a growing role to play in political discourse, including in political campaigns, voter mobilization ventures, and dissemination of political news, though chatbots in the political domain are relatively understudied. While testing the machine heuristics and political self-concepts frameworks, we carried out a 2 × 2 experiment where both perceived conversational partner (i.e., bot, human) and topic (i.e., political, casual) were manipulated ( N = 126). During the experiment, participants exchanged chat messages with trained research confederates for 30 min. In support of the machine heuristics and political self-concepts frameworks, participants assigned to human partners reported more positive relationships and higher political interest. Through moderation analysis, liking the partner was found to differ between the perceived partner conditions, with perceived political knowledge varying more in the human conditions. Thus, the experimental findings add nuance to interpersonal (i.e., impression management and social identity theory) and human-computer interaction theories (i.e., machine heuristics and Computers Are Social Actors), and have broader implications for online political interactions and for decisionmakers of online political discourse spaces.
{"title":"Human or Not?: An Experiment With Chatbot Manipulations to Test Machine Heuristics and Political Self-Concepts","authors":"Ke M. Huang-Isherwood, Jaeho Cho, Joo-Wha Hong, Eugene Lee","doi":"10.1177/08944393241252027","DOIUrl":"https://doi.org/10.1177/08944393241252027","url":null,"abstract":"Chatbots have a growing role to play in political discourse, including in political campaigns, voter mobilization ventures, and dissemination of political news, though chatbots in the political domain are relatively understudied. While testing the machine heuristics and political self-concepts frameworks, we carried out a 2 × 2 experiment where both perceived conversational partner (i.e., bot, human) and topic (i.e., political, casual) were manipulated ( N = 126). During the experiment, participants exchanged chat messages with trained research confederates for 30 min. In support of the machine heuristics and political self-concepts frameworks, participants assigned to human partners reported more positive relationships and higher political interest. Through moderation analysis, liking the partner was found to differ between the perceived partner conditions, with perceived political knowledge varying more in the human conditions. Thus, the experimental findings add nuance to interpersonal (i.e., impression management and social identity theory) and human-computer interaction theories (i.e., machine heuristics and Computers Are Social Actors), and have broader implications for online political interactions and for decisionmakers of online political discourse spaces.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"17 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140895783","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 : 2024-05-07DOI: 10.1177/08944393241252639
Alejandra Castillo Ara
Over the last few years, Chile’s judicial system has witnessed a rise in criminal assumptions generated through social networks, under its hypotheses of funas, doxing, flagging or, in general, the exposure of the personal data of an individual, whether motivated by the performance of conduct of criminal relevance or simply of dubious morality or social appropriateness. Although these conducts originated as a form of digital social empowerment, they have turned into a criminal matter on their own right, posing a series of issues regarding the correct application of the law and casting doubt on the appropriate legal mechanisms to handle these types of accusations, as cases involving these kinds of conducts have been resolved both on a criminal and constitutional level. Thus, the purpose of this article is to determine whether the current Chilean national regulation provides sufficient tools that, with a reasonable interpretation, allow it to correctly handle these sorts of cases. Moreover, this article aims to, through a comprehensive analysis of Germany’s current legislation, determine whether Chile requires a new all-encompassing regulatory approach to these hypotheses similar to the German solution, or if the current rules provide a proper solution to the problem of public personal data exposure on social media as a dangerous behaviour for the person whose data is being exposed.
{"title":"Use and Abuse of Social Media as a Punitive Remedy in Light of Criminal Law: A Tool or a Court? Analysis of the Chilean Regulation","authors":"Alejandra Castillo Ara","doi":"10.1177/08944393241252639","DOIUrl":"https://doi.org/10.1177/08944393241252639","url":null,"abstract":"Over the last few years, Chile’s judicial system has witnessed a rise in criminal assumptions generated through social networks, under its hypotheses of funas, doxing, flagging or, in general, the exposure of the personal data of an individual, whether motivated by the performance of conduct of criminal relevance or simply of dubious morality or social appropriateness. Although these conducts originated as a form of digital social empowerment, they have turned into a criminal matter on their own right, posing a series of issues regarding the correct application of the law and casting doubt on the appropriate legal mechanisms to handle these types of accusations, as cases involving these kinds of conducts have been resolved both on a criminal and constitutional level. Thus, the purpose of this article is to determine whether the current Chilean national regulation provides sufficient tools that, with a reasonable interpretation, allow it to correctly handle these sorts of cases. Moreover, this article aims to, through a comprehensive analysis of Germany’s current legislation, determine whether Chile requires a new all-encompassing regulatory approach to these hypotheses similar to the German solution, or if the current rules provide a proper solution to the problem of public personal data exposure on social media as a dangerous behaviour for the person whose data is being exposed.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"88 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140895795","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 : 2024-05-01DOI: 10.1177/08944393241249725
Luke Ryan Mungall, Scott Pruysers, Julie Blais
Many forms of online political incivility threaten democratic norms, contribute to polarization, and are often directed at women and racial minorities. Recent research shows that online political incivility may come from a minority of users that are just as hostile offline as they are online, meaning that individual differences in personality traits may be an important predictor of online political incivility. Drawing upon a large sample of adults living in Canada (N = 1725), we examined the association between personality traits and online political incivility using robust measures of psychopathy, narcissism, Machiavellianism, and the general traits of the HEXACO. While controlling for a variety of sociodemographic and political variables, we found that people who score higher in honesty-humility, agreeableness, and conscientiousness, as well as the planfulness facet of Machiavellianism, are less likely to report engagement in online political incivility. People who score higher in extraversion, several facets of psychopathy, grandiose and vulnerable narcissism, and antagonistic Machiavellianism, by contrast, are more likely to report engagement in online political incivility. In general, the personality traits that predict offline aggression and antisocial behaviour tend to be the same traits that predict self-reports of vulgarity, stereotyping, and threats in online political discussions. Interventions to reduce online incivility may benefit from considering the dispositional tendencies of uncivil users.
{"title":"Online and Unkind: Examining the Personality Correlates of Online Political Incivility","authors":"Luke Ryan Mungall, Scott Pruysers, Julie Blais","doi":"10.1177/08944393241249725","DOIUrl":"https://doi.org/10.1177/08944393241249725","url":null,"abstract":"Many forms of online political incivility threaten democratic norms, contribute to polarization, and are often directed at women and racial minorities. Recent research shows that online political incivility may come from a minority of users that are just as hostile offline as they are online, meaning that individual differences in personality traits may be an important predictor of online political incivility. Drawing upon a large sample of adults living in Canada (N = 1725), we examined the association between personality traits and online political incivility using robust measures of psychopathy, narcissism, Machiavellianism, and the general traits of the HEXACO. While controlling for a variety of sociodemographic and political variables, we found that people who score higher in honesty-humility, agreeableness, and conscientiousness, as well as the planfulness facet of Machiavellianism, are less likely to report engagement in online political incivility. People who score higher in extraversion, several facets of psychopathy, grandiose and vulnerable narcissism, and antagonistic Machiavellianism, by contrast, are more likely to report engagement in online political incivility. In general, the personality traits that predict offline aggression and antisocial behaviour tend to be the same traits that predict self-reports of vulgarity, stereotyping, and threats in online political discussions. Interventions to reduce online incivility may benefit from considering the dispositional tendencies of uncivil users.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"1 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140819987","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 : 2024-05-01DOI: 10.1177/08944393241249708
Rens Vliegenthart, Jade Vrielink, Katharine Dommett, Rachel Gibson, Esmeralda Bon, Xiaotong Chu, Claes de Vreese, Sophie Lecheler, Jörg Matthes, Sophie Minihold, Lukas Otto, Marlis Stubenvoll, Sanne Kruikemeier
This paper investigates how the acceptance of data-driven political campaigning depends on four different message characteristics. A vignette study was conducted in 25 countries with a total of 14,390 respondents who all evaluated multiple descriptions of political advertisements. Relying on multi-level models, we find that in particular the source and the issue of the message matters. Messages that are sent by a party the respondent likes and deal with a political issue the respondent considers important are rated more acceptable. Furthermore, targeting based on general characteristics instead of individual ones is considered more acceptable, as is a general call to participate in the upcoming elections instead of a specific call to vote for a certain party. Effects differ across regulatory contexts, with the negative impact of both individual targeting and a specific call to vote for a certain party being in countries that have higher levels of legislative regulation.
{"title":"Citizens’ Acceptance of Data-Driven Political Campaigning: A 25-Country Cross-National Vignette Study","authors":"Rens Vliegenthart, Jade Vrielink, Katharine Dommett, Rachel Gibson, Esmeralda Bon, Xiaotong Chu, Claes de Vreese, Sophie Lecheler, Jörg Matthes, Sophie Minihold, Lukas Otto, Marlis Stubenvoll, Sanne Kruikemeier","doi":"10.1177/08944393241249708","DOIUrl":"https://doi.org/10.1177/08944393241249708","url":null,"abstract":"This paper investigates how the acceptance of data-driven political campaigning depends on four different message characteristics. A vignette study was conducted in 25 countries with a total of 14,390 respondents who all evaluated multiple descriptions of political advertisements. Relying on multi-level models, we find that in particular the source and the issue of the message matters. Messages that are sent by a party the respondent likes and deal with a political issue the respondent considers important are rated more acceptable. Furthermore, targeting based on general characteristics instead of individual ones is considered more acceptable, as is a general call to participate in the upcoming elections instead of a specific call to vote for a certain party. Effects differ across regulatory contexts, with the negative impact of both individual targeting and a specific call to vote for a certain party being in countries that have higher levels of legislative regulation.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"47 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140819997","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 : 2024-04-29DOI: 10.1177/08944393241246280
Alan G. Isaac
Simulation experiments have increased their influence on social science, creating a need for documentation tools and practices that facilitate replicability. Two crucial components common to many simulation experiments require particularly detailed documentation: the baseline parameterization, and the experimental designs. This paper explores the adaptability to these needs of a recent but already popular file format. It demonstrates how to produce easily human-readable documentation of baseline parameterizations that also supports automated, programming-language agnostic information exchange. This format is additionally useful for documenting certain experimental designs.
{"title":"Documenting and Exchanging Simulation Specifications: A Language-Agnostic Approach","authors":"Alan G. Isaac","doi":"10.1177/08944393241246280","DOIUrl":"https://doi.org/10.1177/08944393241246280","url":null,"abstract":"Simulation experiments have increased their influence on social science, creating a need for documentation tools and practices that facilitate replicability. Two crucial components common to many simulation experiments require particularly detailed documentation: the baseline parameterization, and the experimental designs. This paper explores the adaptability to these needs of a recent but already popular file format. It demonstrates how to produce easily human-readable documentation of baseline parameterizations that also supports automated, programming-language agnostic information exchange. This format is additionally useful for documenting certain experimental designs.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"1 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140820035","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 : 2024-04-27DOI: 10.1177/08944393241245395
Jessica Daikeler, Leon Fröhling, Indira Sen, Lukas Birkenmaier, Tobias Gummer, Jan Schwalbach, Henning Silber, Bernd Weiß, Katrin Weller, Clemens Lechner
While survey data has long been the focus of quantitative social science analyses, observational and content data, although long-established, are gaining renewed attention; especially when this type of data is obtained by and for observing digital content and behavior. Today, digital technologies allow social scientists to track “everyday behavior” and to extract opinions from public discussions on online platforms. These new types of digital traces of human behavior, together with computational methods for analyzing them, have opened new avenues for analyzing, understanding, and addressing social science research questions. However, even the most innovative and extensive amounts of data are hollow if they are not of high quality. But what does data quality mean for modern social science data? To investigate this rather abstract question the present study focuses on four objectives. First, we provide researchers with a decision tree to identify appropriate data quality frameworks for a given use case. Second, we determine which data types and quality dimensions are already addressed in the existing frameworks. Third, we identify gaps with respect to different data types and data quality dimensions within the existing frameworks which need to be filled. And fourth, we provide a detailed literature overview for the intrinsic and extrinsic perspectives on data quality. By conducting a systematic literature review based on text mining methods, we identified and reviewed 58 data quality frameworks. In our decision tree, the three categories, namely, data type, the perspective it takes, and its level of granularity, help researchers to find appropriate data quality frameworks. We, furthermore, discovered gaps in the available frameworks with respect to visual and especially linked data and point out in our review that even famous frameworks might miss important aspects. The article ends with a critical discussion of the current state of the literature and potential future research avenues.
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