What are the challenges and opportunities for using social data to improve social welfare?
Best insight from top research papers
The challenges and opportunities for using social data to improve social welfare are multifaceted. One challenge is the volume, quality, and context of the available data in social service systems, which require unique considerations . Technical, political, privacy, and ethical challenges also exist in utilizing social data for the greater good . However, the potential benefits are significant. Social data can be leveraged to create a better society, generate competitive industries, and improve citizens' well-being . Machine learning techniques, such as social network analytics, game analytics, and medical analytics, can be effective tools for extracting essential information from large amounts of data . Additionally, the integration of data sets and consideration of positive and negative externalities can contribute to the measurement of net social welfare and the creation of value from data . By addressing these challenges and leveraging the opportunities, social data has the potential to make a positive impact on social welfare.
Answers from top 4 papers
More filters
| Papers (4) | Insight |
|---|---|
| The paper discusses the challenges and opportunities of using social data to improve social welfare, including the trade-offs between positive externalities (such as combining data sets) and negative externalities (such as privacy loss). | |
| Challenges for using social data include technical, political, privacy, and ethical concerns. The paper explores the potential of data donation and participatory design as a solution for utilizing social data for social welfare. | |
| The paper discusses the challenges of using data science tools in the social services sector, including the limitations of small data and the need for refining and leveraging available data. It also highlights the opportunities for data scientists to improve social welfare through collaborations and respecting data limitations. | |
| The paper discusses challenges and opportunities in using machine learning techniques and data analytics for social good, including social network analytics, game analytics, medical analytics, and human resource management analytics. |
Related Questions
What are the challenges of data analysis?4 answersData analysis faces several challenges in its implementation. One of the main challenges is the management of data and information, including issues related to data quality, access, and representation. Another challenge is the formation and management of data analytics teams, which require skilled professionals and effective project management. Additionally, the overall management of data analytics projects, including strategic planning and process optimization, poses significant challenges. The computational and statistical challenges introduced by big data, such as scalability, noise accumulation, and spurious correlation, also impact data analysis. Furthermore, the increasing volume and variety of data, along with the need for efficient analysis of unstructured or semi-structured data, present ongoing challenges. Finally, the integration of data analysis into traditional methodologies and the acceptance of non-randomized intervention studies pose challenges for medical journals and policy makers.
What are some of the challenges in using social media data in tourism?3 answersUsing social media data in tourism presents several challenges. One major challenge is the ambiguity of natural languages, which can cause expressions to have different meanings in different contexts. Another challenge is the limited availability of data in the nascent space tourism industry, making it difficult to conduct empirical studies. Additionally, while online review data has the potential to accurately predict tourism demand, its application in tourism is still limited. Furthermore, the bias towards social media users raises concerns about the representativeness of the entire population when using social media data for tourism statistics. Overall, these challenges highlight the need for context-aware analysis methods, the development of reliable data sources, and the validation of social media data against alternative sources to ensure accurate and representative insights in tourism research.
What are the challenges to using data in research?5 answersBig Data presents several challenges to researchers. Firstly, the size of the data and the growth of data sources pose challenges in terms of storage and processing. Secondly, Big Data is often gathered for purposes other than research, making its fit-for-purpose problematic. Additionally, Big Data may easily lead to overfitting and spuriousness, and biases inherent to Big Data can affect the accuracy of the results. Linkage of data sets also remains problematic. Furthermore, Big Data results are hard to generalize, and working with Big Data may raise new ethical problems. Despite these challenges, Big Data offers opportunities to study previously inaccessible problems with previously inconceivable sources of data. It is important to note that Big Data studies will not replace small data studies, but rather work in concert to have a lasting impact.
What are the challenges and barriers to effective monitoring and evaluation systems for social policies?5 answersEffective monitoring and evaluation (M&E) systems for social policies face several challenges and barriers. These include a lack of knowledge and training among social workers, poor management and supervisor support, and a focus on specific indicators at the expense of understanding system functions and processes. Additionally, there is limited attention to how context affects systems, and the influence of policy on decision-making is not always clear. Another challenge is the prevailing conception that evaluations should assess specific objectives of programs and projects rather than the actual policies themselves. Inadequate infrastructure and tools, as well as a lack of human capacity, hinder fair and credible evaluations in the public sector. To overcome these challenges, it is important to prioritize capacity building in M&E, create sustainable learning processes, and promote transparency in data-sharing and decision-making.
What are the challenges of collecting data for a machine learning model?4 answersData collection for machine learning models presents several challenges. One challenge is the time-consuming and tedious nature of collecting a large number of real images for training an object classification engine. Another challenge is the need to overcome the difficulties associated with correlating and labeling training data, particularly when dealing with one-to-many and direct-to-indirect measurement relationships. Additionally, data collection in machine learning systems often lacks systematic frameworks and procedures, leading to issues related to fairness, accountability, transparency, and ethics. Furthermore, in collaborative learning processes based on heterogeneous data, challenges arise in terms of efficiency, security, and availability in real-world situations. Lastly, when training machine learning models for animal behavior recognition, collecting enough data for training can be problematic, especially when dealing with wild species.
What are the challenges of assessing welfare in practice?0 answersAssessing welfare in practice faces several challenges. One challenge is the need for a comprehensive set of welfare indicators that describe the conditions the animals are subjected to, as well as parameters that directly relate to the animals themselves or their behavior. Another challenge is the subjective and unreliable nature of many current methods used for estimating animal welfare, which can lead to inaccurate results. Additionally, assessing welfare in specific contexts, such as out-wintering beef cows, requires considering the potential trade-offs between reduced housing costs and potential welfare challenges. Furthermore, the meat industry faces challenges in assessing welfare at the time of slaughter, including the effects of transport, reactions to novel environments, and commercial pressures. Overall, a combined approach that considers both input-based and outcome-based indicators, as well as the specific context and challenges faced, is necessary for accurate welfare assessment.