Building social computing system in big data: From the perspective of social network analysis

Liao, Chien Hsiang; Chen, Mu-Yen

doi:10.1016/j.chb.2018.09.040

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…Public policy is a form of a collection of several sets of actions, plans, laws and behaviors for decision making by the government and other political actors as an effort to influence, change, or overcome a problem or issue that is recognized as having entered the political realm by policy makers and larger community [5] [6]. Meanwhile, social computing is a hybrid of the field of computer science that is connected with the analysis of social data (which includes politics, economics, socio-culture, and defense and security) and computing based on Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning technology [7]. In this case, the social data can come from various kinds of sources, such as social media (twitter, YouTube, Facebook, Instagram and others).…”

Section: A Public Policy and Social Computingmentioning

confidence: 99%

Social Computing to Create Government Public Policy Document Blueprint Draft Based on Social Media Data About Covid-19 Using LSTM and MMR Hybrid Algorithms

Cholissodin

2021

ICGT

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Abstract- Determining a policy is often limited in a short time so that decisions are prone to inaccuracies and are ultimately judged to be less targeted. Therefore, there is a necessity to use data mining technology. Currently, especially due to the continuously increasing case of the spread of COVID-19 in Indonesia, in order to reduce the rate of spread of COVID-19, the government has established a COVID-19 vaccination and emergency Community Activity Restriction Implementation (PPKM) policies. For the success of the policies, the government is required to ascertain and understand the attitude of the society. Hence, the policies can be accepted and supported by the society. YouTube is one of the sources to discover people’s attitudes because in YouTube, people can express their opinions freely. In this study, a model based on Natural Language Processing (NLP) with the Deep Learning method was developed to analyze people’s attitudes from their writings or posts on social media. As for the algorithm stages, first the model analysis was created using the Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM) algorithms as a comparison. In the COVID-19 vaccination policy, the Bi-LSTM algorithm provided a better evaluation value, i.e., the accuracy value of 87.20%, recall of 87.14%, precision of 87.14%, and F1-score of 87.14%. In the emergency PPKM policy, the LSTM algorithm provided a better evaluation value of 95.13%, recall of 93.94%, precision of 94.08%, and F1-score of 94.01%. In addition, using the Maximum Marginal Relevance (MMR) method to obtain recommendations related to the COVID-19 vaccination policy, the result showed that the government needs to carry out re-socialization regarding vaccination and the impact of the vaccine so that the society can become more cooperative and the emergency PPKM policy needs to be reviewed because it has an impact on the society’s economy.

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Section: A Public Policy and Social Computingmentioning

confidence: 99%

Social Computing to Create Government Public Policy Document Blueprint Draft Based on Social Media Data About Covid-19 Using LSTM and MMR Hybrid Algorithms

Cholissodin

2021

ICGT

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“…The idea of a social network and its analysis has existed for more than 100 years, but it was not until the second half of the 20th century that scientists began to deal with it. With the advent of computers, SNA expanded into a discipline with its own approach to this paradigm, including clearly defined theory and analytical software [35,36,37].…”

Section: A Customer Data Collectionmentioning

confidence: 99%

Computer Estimation of Customer Similarity With Facebook Lookalikes: Advantages and Disadvantages of Hyper-Targeting

Semerádová

Weinlich

2019

IEEE Access

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The advertising systems and the algorithms they use are constantly evolving and expanding the possibilities for reaching potential customers. Hyper-targeting (also called microtargeting) is the use of detailed customer data and marketing automation to deliver highly targeted and personalized messages across a large number of channels. These campaigns are designed to appeal to specific people or small groups of customers. By using the ability to process large amounts of data through innovations, such as predictive analytics, marketers can gain a deeper understanding of their audiences, focusing on specific accounts and not on the entire segments. This reportedly allows B2B brands to target customers directly and provide unique personal and highly relevant experiences. However, the scientific evidence to support this claim is missing. Some previous studies even suggest a negative impact of highly personalized advertising content on user reactiveness and purchase behavior. In this article, we test the effects of different levels of personalized advertisements using the advanced campaign targeting tool called Facebook Lookalike Audiences. Facebook Lookalike Audiences works on the basis of the estimation of customer similarity based on the characteristics of a custom audience, as defined by the advertiser. We examine the performance of various targeting settings using data from 840 Facebook ads with different personalization levels. These advertisements are compared in terms of reach, number of reactions, frequency of impressions, number of clicks, average time spent on a website, number of viewed pages, number of conversions, and profitability. We believe that the findings presented in this article help clarify the factors that influence user reactiveness toward personalized online advertising using evidence from actual Facebook ad sets.

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“…Therefore, the era of big data has given rise to the openness of data. Open data relies on the big data environment to generate, exist, and develop, forming an inherent relationship between demand and supply [3]. At present, the social problems involved in open data clearly reflect the publicity and service of public policies.…”

Section: Introductionmentioning

confidence: 99%

Construction of public policy decision-making model based on big data analysis from the perspective of sustainable development

2023

Second International Conference on Sustainable Technology and Management (ICSTM 2023)

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In the complex and changeable social transition period, public policy, as one of the important means for the government to regulate the interest relationship among social members and realize the rational distribution of public interests and specific administrative objectives, is playing an increasingly important role. China is currently in the period of economic transition, which inevitably involves how to use a reasonable decision-making model to formulate scientific and effective public policies. Based on the perspective of public policy cycle theory, this article proposes an analytical framework for the impact and application of artificial intelligence algorithms on four stages of public policy: problem definition and agenda setting, policy formulation, policy execution, and policy evaluation. It points out that artificial intelligence algorithms have played a huge role in improving the scientific, accurate, and effective nature of public decision-making through their big data processing and predictive analysis capabilities. We have constructed the "S-R-P" theoretical model of China's open data policy in the big data environment, as well as its demand layer, decision-making layer, and operation layer submodels, to adjust existing policy demands through the operation of two circular paths. The result of model operation is not necessarily the introduction of specialized open data policies, which is also the essential difference between the "S-R-P" theoretical model and the general public policy theoretical model.

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Building social computing system in big data: From the perspective of social network analysis

Cited by 8 publications

References 40 publications

Social Computing to Create Government Public Policy Document Blueprint Draft Based on Social Media Data About Covid-19 Using LSTM and MMR Hybrid Algorithms

Social Computing to Create Government Public Policy Document Blueprint Draft Based on Social Media Data About Covid-19 Using LSTM and MMR Hybrid Algorithms

Computer Estimation of Customer Similarity With Facebook Lookalikes: Advantages and Disadvantages of Hyper-Targeting

Construction of public policy decision-making model based on big data analysis from the perspective of sustainable development

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