Pub Date : 2024-08-06DOI: 10.1177/08944393241269415
Elia A. G. Arfini, Luigi Curini, Fabiana G. Giannuzzi
Acknowledging the importance of focusing on media’s communication for studying linguistic sexism, we propose a new method to analyze a corpus of texts via a machine learning approach built around an original training-set. We seek to establish a framework of the current use of talking about women in newspapers that expands beyond merely the objective forms of discrimination by also measuring the degree to which it implicitly conveys sexist messages through combination of words, expressions, and lexical aspects of language. As an illustrative example, we then apply such an approach to around 15,000 Italian newspapers’ headlines to investigate the impact of newspapers’ political orientations on the linguistic choices made by journalists in writing articles’ headlines.
{"title":"Sexism and Media Communication. An Application to the Italian Case","authors":"Elia A. G. Arfini, Luigi Curini, Fabiana G. Giannuzzi","doi":"10.1177/08944393241269415","DOIUrl":"https://doi.org/10.1177/08944393241269415","url":null,"abstract":"Acknowledging the importance of focusing on media’s communication for studying linguistic sexism, we propose a new method to analyze a corpus of texts via a machine learning approach built around an original training-set. We seek to establish a framework of the current use of talking about women in newspapers that expands beyond merely the objective forms of discrimination by also measuring the degree to which it implicitly conveys sexist messages through combination of words, expressions, and lexical aspects of language. As an illustrative example, we then apply such an approach to around 15,000 Italian newspapers’ headlines to investigate the impact of newspapers’ political orientations on the linguistic choices made by journalists in writing articles’ headlines.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"2 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141899594","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 : 2024-08-05DOI: 10.1177/08944393241269417
Maria Iranzo-Cabrera, Maria Jose Castro-Bleda, Iris Simón-Astudillo, Lluís-F. Hurtado
Social media has led to a redefinition of the journalist’s role. Specifically on Twitter, these professionals assume an influential position and their discourse is dominated by personal opinions. Taking into consideration that this platform has proven to be a breeding ground for polarization, digital harassment and hate speech, notably against women politicians, this research aims to analyze journalists’ involvement in this complex scenario. The investigation aims to determine whether, immersed in online and gender defamation campaigns, journalists enhance the quality of public debate or, on the contrary, they reinforce the visibility of this hostile content. To this end, we examined a sample of 63,926 tweets published from 23 to 25 November 2022 related to a campaign of political violence against the Spanish Minister of Equality using Natural Language Processing tools and qualitative content analysis. Results show that during those three days, at least half of the tweets contained hate speech and improper language. In this climate of hostility, journalists participating in the debate not only have an ability to attract likes and retweets but also exhibit polarization and use hate speech. Each ideological position—for and against the Minister—is also reflected in their own uncivil strategies. Under the umbrella of free speech and regardless of argumentative discourses, those journalists who lean towards ideological progressivism tend to insult their opponents, and those on the political right use divisive constructions, stereotyping and irony as attack techniques.
{"title":"Journalists’ Ethical Responsibility: Tackling Hate Speech Against Women Politicians in Social Media Through Natural Language Processing Techniques","authors":"Maria Iranzo-Cabrera, Maria Jose Castro-Bleda, Iris Simón-Astudillo, Lluís-F. Hurtado","doi":"10.1177/08944393241269417","DOIUrl":"https://doi.org/10.1177/08944393241269417","url":null,"abstract":"Social media has led to a redefinition of the journalist’s role. Specifically on Twitter, these professionals assume an influential position and their discourse is dominated by personal opinions. Taking into consideration that this platform has proven to be a breeding ground for polarization, digital harassment and hate speech, notably against women politicians, this research aims to analyze journalists’ involvement in this complex scenario. The investigation aims to determine whether, immersed in online and gender defamation campaigns, journalists enhance the quality of public debate or, on the contrary, they reinforce the visibility of this hostile content. To this end, we examined a sample of 63,926 tweets published from 23 to 25 November 2022 related to a campaign of political violence against the Spanish Minister of Equality using Natural Language Processing tools and qualitative content analysis. Results show that during those three days, at least half of the tweets contained hate speech and improper language. In this climate of hostility, journalists participating in the debate not only have an ability to attract likes and retweets but also exhibit polarization and use hate speech. Each ideological position—for and against the Minister—is also reflected in their own uncivil strategies. Under the umbrella of free speech and regardless of argumentative discourses, those journalists who lean towards ideological progressivism tend to insult their opponents, and those on the political right use divisive constructions, stereotyping and irony as attack techniques.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"55 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141895575","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 : 2024-08-02DOI: 10.1177/08944393241269394
Noel George, Azhar Sham, Thanvi Ajith, Marco Bastos
Successful disinformation campaigns depend on the availability of fake social media profiles used for coordinated inauthentic behavior with networks of false accounts including bots, trolls, and sockpuppets. This study presents a scalable and unsupervised framework to identify visual elements in user profiles strategically exploited in nearly 60 influence operations, including camera angle, photo composition, gender, and race, but also more context-dependent categories like sensuality and emotion. We leverage Google’s Teachable Machine and the DeepFace Library to classify fake user accounts in the Twitter Moderation Research Consortium database, a large repository of social media accounts linked to foreign influence operations. We discuss the performance of these classifiers against manually coded data and their applicability in large-scale data analysis. The proposed framework demonstrates promising results for the identification of fake online profiles used in influence operations and by the cottage industry specialized in crafting desirable online personas.
{"title":"Forty Thousand Fake Twitter Profiles: A Computational Framework for the Visual Analysis of Social Media Propaganda","authors":"Noel George, Azhar Sham, Thanvi Ajith, Marco Bastos","doi":"10.1177/08944393241269394","DOIUrl":"https://doi.org/10.1177/08944393241269394","url":null,"abstract":"Successful disinformation campaigns depend on the availability of fake social media profiles used for coordinated inauthentic behavior with networks of false accounts including bots, trolls, and sockpuppets. This study presents a scalable and unsupervised framework to identify visual elements in user profiles strategically exploited in nearly 60 influence operations, including camera angle, photo composition, gender, and race, but also more context-dependent categories like sensuality and emotion. We leverage Google’s Teachable Machine and the DeepFace Library to classify fake user accounts in the Twitter Moderation Research Consortium database, a large repository of social media accounts linked to foreign influence operations. We discuss the performance of these classifiers against manually coded data and their applicability in large-scale data analysis. The proposed framework demonstrates promising results for the identification of fake online profiles used in influence operations and by the cottage industry specialized in crafting desirable online personas.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"75 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141880310","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 : 2024-07-31DOI: 10.1177/08944393241269097
Emanuele Brugnoli, Rosaria Simone, Marco Delmastro
The media attention to the personal sphere of famous and important individuals has become a key element of the gender narrative. In this setting, we aim at assessing gender gaps in the mediated personalization of a wide range of political office holders in Italy during the period 2017–2020 by means of a combination of NLP and statistical methods. The proposed analysis hinges on the definition of a new score for each word in the corpus that adjusts the incidence rate for the under representation of women in politics. On this basis, evidence is found that political personalization in Italy is more detrimental for women than it is for men, with the persistence of entrenched stereotypes including a masculine connotation of leadership, the resulting women’s unsuitability to hold political functions, and a greater deal of focus on their attractiveness and body parts. In addition, women politicians are covered with a more negative tone than their men counterpart when personal details are reported. By distinguishing between different types of media, we also show that the observed gender differences are primarily found in online news rather than print news. This suggests that the expression of certain stereotypes may be favored when click baiting and personal targeting have a major impact.
{"title":"Combining Natural Language Processing and Statistical Methods to Assess Gender Gaps in the Mediated Personalization of Politics","authors":"Emanuele Brugnoli, Rosaria Simone, Marco Delmastro","doi":"10.1177/08944393241269097","DOIUrl":"https://doi.org/10.1177/08944393241269097","url":null,"abstract":"The media attention to the personal sphere of famous and important individuals has become a key element of the gender narrative. In this setting, we aim at assessing gender gaps in the mediated personalization of a wide range of political office holders in Italy during the period 2017–2020 by means of a combination of NLP and statistical methods. The proposed analysis hinges on the definition of a new score for each word in the corpus that adjusts the incidence rate for the under representation of women in politics. On this basis, evidence is found that political personalization in Italy is more detrimental for women than it is for men, with the persistence of entrenched stereotypes including a masculine connotation of leadership, the resulting women’s unsuitability to hold political functions, and a greater deal of focus on their attractiveness and body parts. In addition, women politicians are covered with a more negative tone than their men counterpart when personal details are reported. By distinguishing between different types of media, we also show that the observed gender differences are primarily found in online news rather than print news. This suggests that the expression of certain stereotypes may be favored when click baiting and personal targeting have a major impact.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"178 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141877344","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 : 2024-07-30DOI: 10.1177/08944393231225547
Donghee Shin, Kulsawasd Jitkajornwanich
Algorithmic radicalization is the idea that algorithms used by social media platforms push people down digital “rabbit holes” by framing personal online activity. Algorithms control what people see and when they see it and learn from their past activities. As such, people gradually and subconsciously adopt the ideas presented to them by the rabbit hole down which they have been pushed. In this study, TikTok’s role in fostering radicalized ideology is examined to offer a critical analysis of the state of radicalism and extremism on platforms. This study conducted an algorithm audit of the role of radicalizing information in social media by examining how TikTok’s algorithms are being used to radicalize, polarize, and spread extremism and societal instability. The results revealed that the pathways through which users access far-right content are manifold and that a large portion of the content can be ascribed to platform recommendations through radicalization pipelines. Algorithms are not simple tools that offer personalized services but rather contributors to radicalism, societal violence, and polarization. Such personalization processes have been instrumental in how artificial intelligence (AI) has been deployed, designed, and used to the detrimental outcomes that it has generated. Thus, the generation and adoption of extreme content on TikTok are, by and large, not only a reflection of user inputs and interactions with the platform but also the platform’s ability to slot users into specific categories and reinforce their ideas.
{"title":"How Algorithms Promote Self-Radicalization: Audit of TikTok’s Algorithm Using a Reverse Engineering Method","authors":"Donghee Shin, Kulsawasd Jitkajornwanich","doi":"10.1177/08944393231225547","DOIUrl":"https://doi.org/10.1177/08944393231225547","url":null,"abstract":"Algorithmic radicalization is the idea that algorithms used by social media platforms push people down digital “rabbit holes” by framing personal online activity. Algorithms control what people see and when they see it and learn from their past activities. As such, people gradually and subconsciously adopt the ideas presented to them by the rabbit hole down which they have been pushed. In this study, TikTok’s role in fostering radicalized ideology is examined to offer a critical analysis of the state of radicalism and extremism on platforms. This study conducted an algorithm audit of the role of radicalizing information in social media by examining how TikTok’s algorithms are being used to radicalize, polarize, and spread extremism and societal instability. The results revealed that the pathways through which users access far-right content are manifold and that a large portion of the content can be ascribed to platform recommendations through radicalization pipelines. Algorithms are not simple tools that offer personalized services but rather contributors to radicalism, societal violence, and polarization. Such personalization processes have been instrumental in how artificial intelligence (AI) has been deployed, designed, and used to the detrimental outcomes that it has generated. Thus, the generation and adoption of extreme content on TikTok are, by and large, not only a reflection of user inputs and interactions with the platform but also the platform’s ability to slot users into specific categories and reinforce their ideas.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"26 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141857972","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 : 2024-07-30DOI: 10.1177/08944393241268461
Mao Li, Frederick Conrad
From the start of data collection for the 2020 US Census, official and celebrity users tweeted about the importance of everyone being counted in the Census and urged followers to complete the questionnaire (so-called social media campaign.) At the same time, social media posts expressing skepticism about the Census became increasingly common. This study distinguishes between different prototypical Twitter user groups and investigates their possible impact on (online) self-completion rate for the 2020 Census, according to Census Bureau data. Using a network analysis method, Community Detection, and a clustering algorithm, Latent Dirichlet Allocation (LDA), three prototypical user groups were identified: “Official Government Agency,” “Census Advocate,” and “Census Skeptic.” The prototypical Census Skeptic user was motivated by events about which an influential person had tweeted (e.g., “Republicans in Congress signal Census cannot take extra time to count”). This group became the largest one over the study period. The prototypical Census Advocate was motivated more by official tweets and was more active than the prototypical Census Skeptic. The Official Government Agency user group was the smallest of the three, but their messages—primarily promoting completion of the Census—seemed to have been amplified by Census Advocate, especially celebrities and politicians. We found that the daily size of the Census Advocate user group—but not the other two—predicted the 2020 Census online self-completion rate within five days after a tweet was posted. This finding suggests that the Census social media campaign was successful in promoting completion, apparently due to the help of Census Advocate users who encouraged people to fill out the Census and amplified official tweets. This finding demonstrates that a social media campaign can positively affect public behavior regarding an essential national project like the Decennial Census.
{"title":"Tracking Census Online Self-Completion Using Twitter Posts","authors":"Mao Li, Frederick Conrad","doi":"10.1177/08944393241268461","DOIUrl":"https://doi.org/10.1177/08944393241268461","url":null,"abstract":"From the start of data collection for the 2020 US Census, official and celebrity users tweeted about the importance of everyone being counted in the Census and urged followers to complete the questionnaire (so-called social media campaign.) At the same time, social media posts expressing skepticism about the Census became increasingly common. This study distinguishes between different prototypical Twitter user groups and investigates their possible impact on (online) self-completion rate for the 2020 Census, according to Census Bureau data. Using a network analysis method, Community Detection, and a clustering algorithm, Latent Dirichlet Allocation (LDA), three prototypical user groups were identified: “Official Government Agency,” “Census Advocate,” and “Census Skeptic.” The prototypical Census Skeptic user was motivated by events about which an influential person had tweeted (e.g., “Republicans in Congress signal Census cannot take extra time to count”). This group became the largest one over the study period. The prototypical Census Advocate was motivated more by official tweets and was more active than the prototypical Census Skeptic. The Official Government Agency user group was the smallest of the three, but their messages—primarily promoting completion of the Census—seemed to have been amplified by Census Advocate, especially celebrities and politicians. We found that the daily size of the Census Advocate user group—but not the other two—predicted the 2020 Census online self-completion rate within five days after a tweet was posted. This finding suggests that the Census social media campaign was successful in promoting completion, apparently due to the help of Census Advocate users who encouraged people to fill out the Census and amplified official tweets. This finding demonstrates that a social media campaign can positively affect public behavior regarding an essential national project like the Decennial Census.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"81 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141857928","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 : 2024-07-24DOI: 10.1177/08944393241266220
Fernanda Barzallo, Maria Baldeon-Calisto, Margorie Pérez, Maria Emilia Moscoso, Danny Navarrete, Daniel Riofrío, Pablo Medina-Peréz, Susana K Lai-Yuen, Diego Benítez, Noel Peréz, Ricardo Flores Moyano, Mateo Fierro
Content analysis of political manifestos is necessary to understand the policies and proposed actions of a party. However, manually labeling political texts is time-consuming and labor-intensive. Transformer networks have become essential tools for automating this task. Nevertheless, these models require extensive datasets to achieve good performance. This can be a limitation in manifesto classification, where the availability of publicly labeled datasets can be scarce. To address this challenge, in this work, we developed a Transformer network for the classification of manifestos using a cross-domain training strategy. Using the database of the Comparative Manifesto Project, we implemented a fractional factorial experimental design to determine which Spanish-written manifestos form the best training set for Ecuadorian manifesto labeling. Furthermore, we statistically analyzed which Transformer architecture and preprocessing operations improve the model accuracy. The results indicate that creating a training set with manifestos from Spain and Uruguay, along with implementing stemming and lemmatization preprocessing operations, produces the highest classification accuracy. In addition, we found that the DistilBERT and RoBERTa transformer networks perform statistically similarly and consistently well in manifesto classification. Using the cross-context training strategy, DistilBERT and RoBERTa achieve 60.05% and 57.64% accuracy, respectively, in the classification of the Ecuadorian manifesto. Finally, we investigated the effect of the composition of the training set on performance. The experiments demonstrate that training DistilBERT solely with Ecuadorian manifestos achieves the highest accuracy and F1-score. Furthermore, in the absence of the Ecuadorian dataset, competitive performance is achieved by training the model with datasets from Spain and Uruguay.
要了解一个政党的政策和拟议行动，就必须对政治宣言进行内容分析。然而，手动标注政治文本既耗时又耗力。变压器网络已成为实现这一任务自动化的重要工具。然而，这些模型需要大量的数据集才能实现良好的性能。这在宣言分类中可能是一个限制，因为公开标注的数据集可能很少。为了应对这一挑战，在这项工作中，我们采用跨领域训练策略，开发了一种用于宣言分类的 Transformer 网络。利用比较宣言项目的数据库，我们实施了一个分数因子实验设计，以确定哪些西班牙文撰写的宣言是厄瓜多尔宣言标注的最佳训练集。此外，我们还统计分析了哪些 Transformer 架构和预处理操作可以提高模型的准确性。结果表明，创建一个包含西班牙和乌拉圭宣言的训练集，并实施词干化和词素化预处理操作，能产生最高的分类准确率。此外，我们还发现 DistilBERT 和 RoBERTa 变换器网络在宣言分类方面的表现在统计上相似且一致良好。使用跨语境训练策略，DistilBERT 和 RoBERTa 在厄瓜多尔宣言的分类中分别达到了 60.05% 和 57.64% 的准确率。最后，我们研究了训练集的组成对性能的影响。实验表明，仅使用厄瓜多尔宣言对 DistilBERT 进行训练可获得最高的准确率和 F1 分数。此外，在没有厄瓜多尔数据集的情况下，使用西班牙和乌拉圭的数据集对该模型进行训练，也能获得具有竞争力的性能。
{"title":"A Transformer Model for Manifesto Classification Using Cross-Context Training: An Ecuadorian Case Study","authors":"Fernanda Barzallo, Maria Baldeon-Calisto, Margorie Pérez, Maria Emilia Moscoso, Danny Navarrete, Daniel Riofrío, Pablo Medina-Peréz, Susana K Lai-Yuen, Diego Benítez, Noel Peréz, Ricardo Flores Moyano, Mateo Fierro","doi":"10.1177/08944393241266220","DOIUrl":"https://doi.org/10.1177/08944393241266220","url":null,"abstract":"Content analysis of political manifestos is necessary to understand the policies and proposed actions of a party. However, manually labeling political texts is time-consuming and labor-intensive. Transformer networks have become essential tools for automating this task. Nevertheless, these models require extensive datasets to achieve good performance. This can be a limitation in manifesto classification, where the availability of publicly labeled datasets can be scarce. To address this challenge, in this work, we developed a Transformer network for the classification of manifestos using a cross-domain training strategy. Using the database of the Comparative Manifesto Project, we implemented a fractional factorial experimental design to determine which Spanish-written manifestos form the best training set for Ecuadorian manifesto labeling. Furthermore, we statistically analyzed which Transformer architecture and preprocessing operations improve the model accuracy. The results indicate that creating a training set with manifestos from Spain and Uruguay, along with implementing stemming and lemmatization preprocessing operations, produces the highest classification accuracy. In addition, we found that the DistilBERT and RoBERTa transformer networks perform statistically similarly and consistently well in manifesto classification. Using the cross-context training strategy, DistilBERT and RoBERTa achieve 60.05% and 57.64% accuracy, respectively, in the classification of the Ecuadorian manifesto. Finally, we investigated the effect of the composition of the training set on performance. The experiments demonstrate that training DistilBERT solely with Ecuadorian manifestos achieves the highest accuracy and F1-score. Furthermore, in the absence of the Ecuadorian dataset, competitive performance is achieved by training the model with datasets from Spain and Uruguay.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"53 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141755367","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 : 2024-07-20DOI: 10.1177/08944393241261983
C. Jordan Howell, Saeed Kabiri, Fangzhou Wang, Caitlyn N. Muniz, Eden Kamar, Mahmoud Sharepour, John Cochran, Seyyedeh Masoomeh (Shamila) Shadmanfaat
The current study employs a construct from the criminological literature, thoughtfully reflective decision-making (TRDM), to understand cyber offenders’ decision-making and offer relevant insights to prevent online harassment. Using a sample of Iranian high school students ( N = 366), we employ OLS and SEM to test whether and how TRDM, perceived deterrence, and prior victimization influence the most common forms of online harassment: cyberbullying and cyberstalking. Findings demonstrate cyberbullying and cyberstalking victimization increase engagement in offending behavior while participants’ fear of sanction reduces engagement in both cyberbullying and cyberstalking perpetration. Notably, results demonstrate that TRDM has a direct, mediating, and moderating effect on both forms of offending. TRDM also has an indirect effect on cyberbullying and cyberstalking perpetration through victimization and participants’ perceptions of sanction. Unlike contemporary, pre-dispositional theories of crime, TRDM is dynamic and can be improved via educational programming. We posit that current cyber hygiene campaigns should include elements aimed to improve individuals’ cognitive decision-making capabilities. Guided by theory, and based on the results of the current study, this translational approach could prevent victimization while simultaneously improving other elements of the participants’ life.
本研究采用犯罪学文献中的一个概念--深思熟虑的反思性决策（TRDM）--来理解网络犯罪者的决策，并为预防网络骚扰提供相关见解。我们以伊朗高中生（366 人）为样本，采用 OLS 和 SEM 方法检验 TRDM、感知威慑力和先前受害情况是否以及如何影响最常见的网络骚扰形式：网络欺凌和网络跟踪。研究结果表明，网络欺凌和网络跟踪的受害情况会增加犯罪行为的参与度，而参与者对制裁的恐惧则会减少网络欺凌和网络跟踪行为的参与度。值得注意的是，研究结果表明，TRDM 对这两种形式的犯罪行为都有直接、中介和调节作用。TRDM还通过受害情况和参与者对制裁的看法对网络欺凌和网络跟踪的实施产生间接影响。与当代预设犯罪理论不同，TRDM 是动态的，可以通过教育计划加以改进。我们认为，当前的网络卫生活动应包括旨在提高个人认知决策能力的内容。在理论指导下，根据当前研究的结果，这种转化方法可以在防止受害的同时改善参与者生活的其他方面。
{"title":"Online Harassment: The Mediating and Moderating Role of Thoughtfully Reflective Decision-Making","authors":"C. Jordan Howell, Saeed Kabiri, Fangzhou Wang, Caitlyn N. Muniz, Eden Kamar, Mahmoud Sharepour, John Cochran, Seyyedeh Masoomeh (Shamila) Shadmanfaat","doi":"10.1177/08944393241261983","DOIUrl":"https://doi.org/10.1177/08944393241261983","url":null,"abstract":"The current study employs a construct from the criminological literature, thoughtfully reflective decision-making (TRDM), to understand cyber offenders’ decision-making and offer relevant insights to prevent online harassment. Using a sample of Iranian high school students ( N = 366), we employ OLS and SEM to test whether and how TRDM, perceived deterrence, and prior victimization influence the most common forms of online harassment: cyberbullying and cyberstalking. Findings demonstrate cyberbullying and cyberstalking victimization increase engagement in offending behavior while participants’ fear of sanction reduces engagement in both cyberbullying and cyberstalking perpetration. Notably, results demonstrate that TRDM has a direct, mediating, and moderating effect on both forms of offending. TRDM also has an indirect effect on cyberbullying and cyberstalking perpetration through victimization and participants’ perceptions of sanction. Unlike contemporary, pre-dispositional theories of crime, TRDM is dynamic and can be improved via educational programming. We posit that current cyber hygiene campaigns should include elements aimed to improve individuals’ cognitive decision-making capabilities. Guided by theory, and based on the results of the current study, this translational approach could prevent victimization while simultaneously improving other elements of the participants’ life.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"136 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141730514","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 : 2024-05-28DOI: 10.1177/08944393231160961
Jan Karem Höhne, Konstantin Gavras, Joshua Claassen
The smartphone increase in web surveys, coupled with technological developments, provides novel opportunities for measuring attitudes. For example, smartphones allow the collection of voice instead of text answers by using the built-in microphone. This may facilitate answering questions with open answer formats resulting in richer information and higher data quality. So far, there is only a little body of research investigating voice and text answers to open questions. In this study, we therefore compare the linguistic and content characteristics of voice and text answers to open questions on sensitive topics. For this purpose, we ran an experiment in a smartphone survey ( N = 1001) and randomly assigned respondents to an answer format condition (text or voice). The findings indicate that voice answers have a higher number of words and a higher number of topics than their text counterparts. We find no differences regarding sentiments (or extremity of answers). Our study provides new insights into the linguistic and content characteristics of voice and text answers. Furthermore, it helps to evaluate the usefulness and usability of voice answers for future smartphone surveys.
{"title":"Typing or Speaking? Comparing Text and Voice Answers to Open Questions on Sensitive Topics in Smartphone Surveys","authors":"Jan Karem Höhne, Konstantin Gavras, Joshua Claassen","doi":"10.1177/08944393231160961","DOIUrl":"https://doi.org/10.1177/08944393231160961","url":null,"abstract":"The smartphone increase in web surveys, coupled with technological developments, provides novel opportunities for measuring attitudes. For example, smartphones allow the collection of voice instead of text answers by using the built-in microphone. This may facilitate answering questions with open answer formats resulting in richer information and higher data quality. So far, there is only a little body of research investigating voice and text answers to open questions. In this study, we therefore compare the linguistic and content characteristics of voice and text answers to open questions on sensitive topics. For this purpose, we ran an experiment in a smartphone survey ( N = 1001) and randomly assigned respondents to an answer format condition (text or voice). The findings indicate that voice answers have a higher number of words and a higher number of topics than their text counterparts. We find no differences regarding sentiments (or extremity of answers). Our study provides new insights into the linguistic and content characteristics of voice and text answers. Furthermore, it helps to evaluate the usefulness and usability of voice answers for future smartphone surveys.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"40 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141165132","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 : 2024-05-22DOI: 10.1177/08944393241254464
Alexandru Cernat, Florian Keusch, Ruben L. Bach, Paulina K. Pankowska
Digital trace data are receiving increased attention as a potential way to capture human behavior. Nevertheless, this type of data is far from perfect and may not always provide better data compared to traditional social surveys. In this study we estimate measurement quality of survey and digital trace data on smartphone usage with a MultiTrait MultiMethod (MTMM) model. The experimental design included five topics relating to the use of smartphones (traits) measured with five methods: three different survey scales (a 5- and a 7-point frequency scale and an open-ended question on duration) and two measures from digital trace data (frequency and duration). We show that surveys and digital trace data measures have very low correlation with each other. We also show that all measures are far from perfect and, while digital trace data appears to have often better quality compared to surveys, that is not always the case.
{"title":"Estimating Measurement Quality in Digital Trace Data and Surveys Using the MultiTrait MultiMethod Model","authors":"Alexandru Cernat, Florian Keusch, Ruben L. Bach, Paulina K. Pankowska","doi":"10.1177/08944393241254464","DOIUrl":"https://doi.org/10.1177/08944393241254464","url":null,"abstract":"Digital trace data are receiving increased attention as a potential way to capture human behavior. Nevertheless, this type of data is far from perfect and may not always provide better data compared to traditional social surveys. In this study we estimate measurement quality of survey and digital trace data on smartphone usage with a MultiTrait MultiMethod (MTMM) model. The experimental design included five topics relating to the use of smartphones (traits) measured with five methods: three different survey scales (a 5- and a 7-point frequency scale and an open-ended question on duration) and two measures from digital trace data (frequency and duration). We show that surveys and digital trace data measures have very low correlation with each other. We also show that all measures are far from perfect and, while digital trace data appears to have often better quality compared to surveys, that is not always the case.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"64 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141085419","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}
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