Social Science Computer Review最新文献_第3页

Airbnb on TikTok: Brand Perception Through User Engagement and Sentiment Trends TikTok 上的 Airbnb：通过用户参与和情绪趋势感知品牌

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2024-08-08 DOI: 10.1177/08944393241260242

Julia Marti-Ochoa, Eva Martin-Fuentes, Berta Ferrer-Rosell

{"title":"Airbnb on TikTok: Brand Perception Through User Engagement and Sentiment Trends","authors":"Julia Marti-Ochoa, Eva Martin-Fuentes, Berta Ferrer-Rosell","doi":"10.1177/08944393241260242","DOIUrl":"https://doi.org/10.1177/08944393241260242","url":null,"abstract":"This study delves into Airbnb’s brand presence on TikTok by analyzing textual content in posts, and human audio in videos. This approach aims to decipher the brand narrative and gauge user engagement. In the dynamic realm of social media marketing, TikTok has emerged as a key platform in shaping brand perception. This research specifically concentrates on Airbnb’s content, distinguishing between official narratives and user-generated content (UGC). Notably, themes of “Travel” dominate official posts, contrasting with “Real Estate” and “Business” in UGC. The methodology employed involves advanced data collection techniques, including web scraping for textual data and artificial intelligence for transcribing human audio to text. The findings reveal that UGC commands greater engagement and volume compared to Airbnb’s own brand content, underscoring the increasing significance of user involvement in brand storytelling. An analysis of the study results is conducted using linguistic natural processing (LNP) for the sentiment base, and the vector space model for emotion analysis. Sentiment analysis reveals a predominance of the emotion “happiness” and a significant presence of “surprise” in the posts, both of which are critical for audience engagement. Moreover, the study indicates a high approval rate for Airbnb-related content, reflecting a positive reception of the brand. Additionally, the research observes that influencers, particularly nano influencers, have higher engagement rates, indicating that their authenticity and relatability appeal especially to Generation Z audiences. This study not only sheds light on the intricate relationship between brand narrative, user engagement, and sentiment on TikTok but also offers valuable insights into effective brand image construction and propagation in the digital era, highlighting the importance of diverse emotions in enhancing audience engagement.","PeriodicalId":49509,"journal":{"name":"Social Science Computer Review","volume":"62 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141908959","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}

引用次数: 0

Gender Gap in All Academic Fields Over Time 所有学术领域的性别差距随时间变化

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2024-08-08 DOI: 10.1177/08944393241270633

Dariusz Jemielniak, Maciej Wilamowski

引用次数: 0

Sexism and Media Communication. An Application to the Italian Case 性别歧视与媒体传播。意大利案例的应用

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2024-08-06 DOI: 10.1177/08944393241269415

Elia A. G. Arfini, Luigi Curini, Fabiana G. Giannuzzi

引用次数: 0

Journalists’ Ethical Responsibility: Tackling Hate Speech Against Women Politicians in Social Media Through Natural Language Processing Techniques 记者的道德责任：通过自然语言处理技术应对社交媒体中针对女政治家的仇恨言论

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2024-08-05 DOI: 10.1177/08944393241269417

Maria Iranzo-Cabrera, Maria Jose Castro-Bleda, Iris Simón-Astudillo, Lluís-F. Hurtado

{"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}

引用次数: 0

Forty Thousand Fake Twitter Profiles: A Computational Framework for the Visual Analysis of Social Media Propaganda 四万个虚假 Twitter 简介：社交媒体宣传可视化分析的计算框架

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2024-08-02 DOI: 10.1177/08944393241269394

Noel George, Azhar Sham, Thanvi Ajith, Marco Bastos

引用次数: 0

Combining Natural Language Processing and Statistical Methods to Assess Gender Gaps in the Mediated Personalization of Politics 结合自然语言处理和统计方法，评估政治个性化中介中的性别差距

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2024-07-31 DOI: 10.1177/08944393241269097

Emanuele Brugnoli, Rosaria Simone, Marco Delmastro

{"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}

引用次数: 0

How Algorithms Promote Self-Radicalization: Audit of TikTok’s Algorithm Using a Reverse Engineering Method 算法如何促进自我激进化？使用逆向工程方法审计 TikTok 算法

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2024-07-30 DOI: 10.1177/08944393231225547

Donghee Shin, Kulsawasd Jitkajornwanich

{"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}

引用次数: 0

Tracking Census Online Self-Completion Using Twitter Posts 利用 Twitter 帖子跟踪人口普查在线自我填写情况

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2024-07-30 DOI: 10.1177/08944393241268461

Mao Li, Frederick Conrad

{"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}

引用次数: 0

A Transformer Model for Manifesto Classification Using Cross-Context Training: An Ecuadorian Case Study 利用跨语境训练进行宣言分类的转换器模型：厄瓜多尔案例研究

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2024-07-24 DOI: 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

{"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}

引用次数: 0

Online Harassment: The Mediating and Moderating Role of Thoughtfully Reflective Decision-Making 网络骚扰：深思熟虑的反思性决策的中介和调节作用

IF 4.1 2区社会学

Social Science Computer Review Pub Date : 2024-07-20 DOI: 10.1177/08944393241261983

C. Jordan Howell, Saeed Kabiri, Fangzhou Wang, Caitlyn N. Muniz, Eden Kamar, Mahmoud Sharepour, John Cochran, Seyyedeh Masoomeh (Shamila) Shadmanfaat

{"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}

引用次数: 0