1,920 research outputs found
Teens, Social Media, and Privacy
Teens share a wide range of information about themselves on social media sites; indeed the sites themselves are designed to encourage the sharing of information and the expansion of networks. However, few teens embrace a fully public approach to social media. Instead, they take an array of steps to restrict and prune their profiles, and their patterns of reputation management on social media vary greatly according to their gender and network size
Susceptibility to Social Engineering in Social Networking Sites: The Case of Facebook
Past research has suggested that social engineering poses the most significant security risk. Recent studies have suggested that social networking sites (SNSs) are the most common source of social engineering attacks. The risk of social engineering attacks in SNSs is associated with the difficulty of making accurate judgments regarding source credibility in the virtual environment of SNSs. In this paper, we quantitatively investigate source credibility dimensions in terms of social engineering on Facebook, as well as the source characteristics that influence Facebook users to judge an attacker as credible, therefore making them susceptible to victimization. Moreover, in order to predict users’ susceptibility to social engineering victimization based on their demographics, we investigate the effectiveness of source characteristics on different demographic groups by measuring the consent intentions and behavior responses of users to social engineering requests using a role-play experiment
Introduction to Data Science
This book was developed for ICT/LIS 661: Introduction to Data Science, as offered in the University of Kentucky\u27s School of Information Science. It adapts and expands on openly licensed materials to introduce readers to basic statistical concepts, the R programming language, and philosophical critique of data science.
This open access textbook was supported by the University of Kentucky Libraries Alternative Textbook programhttps://uknowledge.uky.edu/slis_textbooks/1000/thumbnail.jp
Teens, social media, and privacy
This report finds that teens are sharing more information about themselves on social media sites than they have in the past, but they are also taking a variety of technical and non-technical steps to manage the privacy of that information.
Despite taking these privacy-protective actions, teen social media users do not express a high level of concern about third-parties (such as businesses or advertisers) accessing their data; just 9% say they are “very” concerned.
Key findings include:
Teens are sharing more information about themselves on their social media profiles than they did when we last surveyed in 2006:
91% post a photo of themselves, up from 79% in 2006.
71% post their school name, up from 49%.
71% post the city or town where they live, up from 61%.
53% post their email address, up from 29%.
20% post their cell phone number, up from 2%.
60% of teen Facebook users set their Facebook profiles to private (friends only), and most report high levels of confidence in their ability to manage their settings.
56% of teen Facebook users say it’s “not difficult at all” to manage the privacy controls on their Facebook profile.
33% Facebook-using teens say it’s “not too difficult.”
8% of teen Facebook users say that managing their privacy controls is “somewhat difficult,” while less than 1% describe the process as “very difficult.”
Authored by Mary Madden, Amanda Lenhart, Sandra Cortesi, Urs Gasser, Maeve Duggan, and Aaron Smith
Consider the Source: Receiver-Assigned Attributions of Credibility to Influential Bloggers
The purpose of this study is to examine credibility as it pertains to blogging. While studies have traditionally considered credibility in the context of the material being created, this study examines source credibility in the context of the personality creating the material. Therefore, this study functions primarily as an exploratory study and seeks to present an understanding of source credibility from the perspective of the individuals participating in blogging communities cultivated by influential bloggers. An interview questionnaire was specially developed for this study. Ten participants were selected for this study. Eight of them are females, two of the participants are males. All but one of the participants are Caucasians.
The study’s results show that support for attributions of credibility differing based on receiver gender and ethnicity does not exist. However, there was a difference in the types of credible behavior attributed to the bloggers in this study. Responses concerning the male Hispanic blogger indicate credibly behavior oriented toward providing depth of information, whereas responses concerning the White female blogger indicated an inclination toward a community-centric blog focused on providing a broad range of resources. Due to the limited sample size of this study, the ability to make general statements and infer statistical significance is limited, thus relegating this study to being only useful for exploratory purposes. This study’s results, data interpretation, implications, and possibilities for future research are discussed at length
MultiTalk: A Highly-Branching Dialog Testbed for Diverse Conversations
We study conversational dialog in which there are many possible responses to
a given history. We present the MultiTalk Dataset, a corpus of over 320,000
sentences of written conversational dialog that balances a high branching
factor (10) with several conversation turns (6) through selective branch
continuation. We make multiple contributions to study dialog generation in the
highly branching setting. In order to evaluate a diverse set of generations, we
propose a simple scoring algorithm, based on bipartite graph matching, to
optimally incorporate a set of diverse references. We study multiple language
generation tasks at different levels of predictive conversation depth, using
textual attributes induced automatically from pretrained classifiers. Our
culminating task is a challenging theory of mind problem, a controllable
generation task which requires reasoning about the expected reaction of the
listener.Comment: 7 pages, AAAI-2
Student Information Use During the COVID-19 Pandemic
Since early 2020, life for students has changed tremendously. It has been a time of stress, turmoil, and trauma for students. Researchers from a large Midwestern university wanted to determine how student information use has changed during the COVID-19 pandemic. This paper examines the results of a mixed-methods study undertaken in 2021 using surveys and follow-up focus groups to determine if and how student information use has changed. To answer this, we explored student use of news sources, social media sources, political affiliations, and information responses, coupled with to what extent these factors demonstrate or impact potential changes in information use. We also addressed changes in the frequency of use, as well as the types of resources consulted, pertaining to information use of traditional and social media sources
The emerging landscape of Social Media Data Collection: anticipating trends and addressing future challenges
[spa] Las redes sociales se han convertido en una herramienta poderosa para crear y compartir contenido generado por usuarios en todo internet. El amplio uso de las redes sociales ha llevado a generar una enorme cantidad de informaciĂłn, presentando una gran oportunidad para el marketing digital. A travĂ©s de las redes sociales, las empresas pueden llegar a millones de consumidores potenciales y capturar valiosos datos de los consumidores, que se pueden utilizar para optimizar estrategias y acciones de marketing. Los beneficios y desafĂos potenciales de utilizar las redes sociales para el marketing digital tambiĂ©n están creciendo en interĂ©s entre la comunidad acadĂ©mica. Si bien las redes sociales ofrecen a las empresas la oportunidad de llegar a una gran audiencia y recopilar valiosos datos de los consumidores, el volumen de informaciĂłn generada puede llevar a un marketing sin enfoque y consecuencias negativas como la sobrecarga social. Para aprovechar al máximo el marketing en redes sociales, las empresas necesitan recopilar datos confiables para propĂłsitos especĂficos como vender productos, aumentar la conciencia de marca o fomentar el compromiso y para predecir los comportamientos futuros de los consumidores. La disponibilidad de datos de calidad puede ayudar a construir la lealtad a la marca, pero la disposiciĂłn de los consumidores a compartir informaciĂłn depende de su nivel de confianza en la empresa o marca que lo solicita. Por lo tanto, esta tesis tiene como objetivo contribuir a la brecha de investigaciĂłn a travĂ©s del análisis bibliomĂ©trico del campo, el análisis mixto de perfiles y motivaciones de los usuarios que proporcionan sus datos en redes sociales y una comparaciĂłn de algoritmos supervisados y no supervisados para agrupar a los consumidores. Esta investigaciĂłn ha utilizado una base de datos de más de 5,5 millones de colecciones de datos durante un perĂodo de 10 años. Los avances tecnolĂłgicos ahora permiten el análisis sofisticado y las predicciones confiables basadas en los datos capturados, lo que es especialmente Ăştil para el marketing digital. Varios estudios han explorado el marketing digital a travĂ©s de las redes sociales, algunos centrándose en un campo especĂfico, mientras que otros adoptan un enfoque multidisciplinario. Sin embargo, debido a la naturaleza rápidamente evolutiva de la disciplina, se requiere un enfoque bibliomĂ©trico para capturar y sintetizar la informaciĂłn más actualizada y agregar más valor a los estudios en el campo. Por lo tanto, las contribuciones de esta tesis son las siguientes. En primer lugar, proporciona una revisiĂłn exhaustiva de la literatura sobre los mĂ©todos para recopilar datos personales de los consumidores de las redes sociales para el marketing digital y establece las tendencias más relevantes a travĂ©s del análisis de artĂculos significativos, palabras clave, autores, instituciones y paĂses. En segundo lugar, esta tesis identifica los perfiles de usuario que más mienten y por quĂ©. EspecĂficamente, esta investigaciĂłn demuestra que algunos perfiles de usuario están más inclinados a cometer errores, mientras que otros proporcionan informaciĂłn falsa intencionalmente. El estudio tambiĂ©n muestra que las principales motivaciones detrás de proporcionar informaciĂłn falsa incluyen la diversiĂłn y la falta de confianza en las medidas de privacidad y seguridad de los datos. Finalmente, esta tesis tiene como objetivo llenar el vacĂo en la literatura sobre quĂ© algoritmo, supervisado o no supervisado, puede agrupar mejor a los consumidores que proporcionan sus datos en las redes sociales para predecir su comportamiento futuro
Detecting Popularity of Ideas and Individuals in Online Community
Research in the last decade has prioritized the effects of online texts and online behaviors on user information prediction. However, the previous research overlooks the overall meaning of online texts and more detailed features about users’ online behaviors. The purpose of the research is to detect the adopted ideas, the popularity of ideas, and the popularity of individuals by identifying the overall meaning of online texts and the centrality features based on user’s online interactions within an online community.
To gain insights into the research questions, the online discussions on MyStarbucksIdea website is examined in this research. MyStarbucksIdea had launched since 2008 that encouraged people to submit new ideas for improving Starbuck’s products and services. Starbucks had adopted hundreds of ideas from this crowdsourcing platform. Based on the example of the MyStarbucksIdea community, a new document representation approach, Doc2Vec, synthesized with the users’ centrality features was unitized in this research. Additionally, it also is essential to study the surface-level features of online texts, the sentiment features of online texts, and the features of users’ online behaviors to determine the idea adoption as well as the popularity of ideas and individuals in the online community. Furthermore, supervised machine learning approaches, including Logistic Regression, Support Vector Machine, and Random Forest, with the adjustments for the imbalanced classes, served as the classifiers for the experiments.
The results of the experiments showed that the classifications of the idea adoption, the popularity of ideas, and the popularity of individuals were all considered successful. The overall meaning of idea texts and user’s centrality features were most accurate in detecting the adopted ideas and the popularity of ideas. The overall meaning of idea texts and the features of users’ online behaviors were most accurate in detecting the popularity of individuals. These results are in accord with the results of the previous studies, which used behavioral and textual features to predict user information and enhance the previous studies\u27 results by providing the new document embedding approach and the centrality features. The models used in this research can become a much-needed tool for the popularity predictions of future research
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