Modelos de aprendizaje supervisado como apoyo a la toma de decisiones en las organizaciones basados en datos de redes sociales: Una revisión sistemática de la literatura.

Las redes sociales se han convertido en la herramienta de comunicación e interacción más utilizada entre las personas y se han diversificado para cumplir funciones importantes dentro de la organización. En consecuencia, las redes sociales se han vuelto una fuente inmensa de datos que son procesados a través de modelos de aprendizaje supervisado para producir información que sea competente para la toma de decisiones como la predicción de campañas electorales, la predicción de consumo de un producto y/o servicio, la reputación de una empresa entre otros. De manera que el presente estudio tiene como objetivo identificar los modelos de aprendizaje supervisado como apoyo a la toma de decisiones en las organizaciones basados en datos de redes sociales. Para la identificación de modelos de aprendizaje supervisado se realizó una revisión sistemática de la literatura(RSL) en bases de datos reconocidas y revistas indexadas. De un total de 1614 artículos se identificaron 32 artículos que hacen referencia a 6 modelos de aprendizaje supervisado y las funciones que cumplen como apoyo a la toma de decisiones en una organización. Se puede concluir que existen diversos modelos de aprendizaje supervisado siendo el de Support Vector Machine de mayor grado de precisión. También se han encontrado en las investigaciones modelos de: Naive Bayes, Decision Tree, Regression: Logistic y lineal, k-Nearest Neighbors, y finalmente Neural Network.Trabajo de investigaciónLIMAEscuela Profesional de Ingeniería de SistemasTecnología de información e innovación tecnológic

Vaccine knowledge among generation Y Malays in Pulau Pinang : a pilot study

The increasing spread of infectious diseases in Malaysia has resulted the topic of vaccine acceptance and rejection is gaining traction in academia. the recent outbreak of the COVID-19 pandemic undoubtedly has manifested the never-ending debate of the topic of immunisation both academically and in public discourse. Thus, it is appropriate and timely to delve deeper into the ideas, norms, and values that influence people's health preferences and behaviour, particularly in vaccination. This study concerns on the perceived and actual vaccine knowledge of Generation Y Malays aged 25 to 40 years old in Pulau Pinang. It is critical to understand the public discussion vaccines within the framework of social interaction – how vaccines are portrayed and publicly understood. A pilot study was conducted prior to this to evaluate the procedure for participant recruitment, the data collection processes and the usability of the survey questionnaire. The improvements made to the instrument and methodology before beginning the main research study on vaccine knowledge among Malaysians in Pulau Pinang were made in an attempt to contribute to the body of research in this area

Methods for Social Media Monitoring Related to Vaccination: Systematic Scoping Review (Preprint)

BACKGROUND Social media has changed the communication landscape, exposing individuals to an ever-growing amount of information while also allowing them to create and share content. Although vaccine skepticism is not new, social media has amplified public concerns and facilitated their spread globally. Multiple studies have been conducted to monitor vaccination discussions on social media. However, there is currently insufficient evidence on the best methods to perform social media monitoring. OBJECTIVE The aim of this study was to identify the methods most commonly used for monitoring vaccination-related topics on different social media platforms, along with their effectiveness and limitations. METHODS A systematic scoping review was conducted by applying a comprehensive search strategy to multiple databases in December 2018. The articles’ titles, abstracts, and full texts were screened by two reviewers using inclusion and exclusion criteria. After data extraction, a descriptive analysis was performed to summarize the methods used to monitor and analyze social media, including data extraction tools; ethical considerations; search strategies; periods monitored; geolocalization of content; and sentiments, content, and reach analyses. RESULTS This review identified 86 articles on social media monitoring of vaccination, most of which were published after 2015. Although 35 out of the 86 studies used manual browser search tools to collect data from social media, this was time-consuming and only allowed for the analysis of small samples compared to social media application program interfaces or automated monitoring tools. Although simple search strategies were considered less precise, only 10 out of the 86 studies used comprehensive lists of keywords (eg, with hashtags or words related to specific events or concerns). Partly due to privacy settings, geolocalization of data was extremely difficult to obtain, limiting the possibility of performing country-specific analyses. Finally, 20 out of the 86 studies performed trend or content analyses, whereas most of the studies (70%, 60/86) analyzed sentiments toward vaccination. Automated sentiment analyses, performed using leverage, supervised machine learning, or automated software, were fast and provided strong and accurate results. Most studies focused on negative (n=33) and positive (n=31) sentiments toward vaccination, and may have failed to capture the nuances and complexity of emotions around vaccination. Finally, 49 out of the 86 studies determined the reach of social media posts by looking at numbers of followers and engagement (eg, retweets, shares, likes). CONCLUSIONS Social media monitoring still constitutes a new means to research and understand public sentiments around vaccination. A wide range of methods are currently used by researchers. Future research should focus on evaluating these methods to offer more evidence and support the development of social media monitoring as a valuable research design. </sec

Methods for Social Media Monitoring Related to Vaccination: Systematic Scoping Review

This study aims to identify the methods most commonly used for monitoring different social media platforms around vaccination, their effectiveness and limitations. A systematic scoping review was conducted by applying a comprehensive search strategy to multiple databases in December 2018. The articles’ titles, abstracts and full texts were screened by two reviewers using inclusion and exclusion criteria. After data extraction, a descriptive analysis was performed to summarize the methods used to monitor and analyze social media, including data extraction tools, ethical considerations, search strategies, periods monitored, geo-localization of content, and sentiments, content and reach analyzes

Vaccine movements on social media: A visual and network analysis

Vaccines are considered one of the most effective public health interventions, but they have been subject to opposition since they were first proposed. Anti-vaccine activists disseminate and sensationalise objections to vaccinations through various channels, including the internet and social media outlets, such as Twitter. These means allow them to reach the public directly and potentially influence their intention to vaccinate. Twitter allows users to share short textual messages and images. Although, images have strong communicative power, there is a lack of research on the networks and actors sharing vaccine images. Moreover, there are no studies on the meaning and messages of these images. Therefore, this study aimed to investigate the dissemination, content, and meaning of anti- and pro-vaccine images in relation to their respective Twitter networks. A mixed methods approach was used to address the research aims, comprising social network analysis, visual content analysis, semiotics and visual social semiotics analyses. Anti-vaccine users re-shared images with each other; they provided support and strengthened their anti-vaccination beliefs. Some key actors, primarily activists and parents, influenced the information flow within the community. Anti-vaccine images claimed that vaccines are not safe, advocated against mandatory vaccinations and promoted vaccine conspiracy theories. They also provided alternative sources of information or pseudoscientific evidence supporting their messages while increasing distrust in traditional experts. The pro-vaccine users form loose connections that favour the dissemination of new vaccine information and networking. In this network, Non-Governmental Organisations (NGOs) and public health organisations influenced the dissemination of images, and the images mostly featured NGO campaigns and achievements in developing countries or promoted the flu vaccine in Western countries. In conclusion, anti- and pro-vaccine networks are insular and share different images in different ways; they use different visual communication strategies to reach their audiences. This resulted in a lack of a middle ground in visual communication of vaccines on Twitter. Addressing this gap could be an opportunity for future immunisation campaigns