A review of social media-based public opinion analyses: Challenges and recommendations
Introduction
Public opinions about policies and events have attracted the attention of scholars and policymakers for a long time [1,2], and they are considered important evidence in making and adjusting decisions [3,4] and an essential concept in the underpinnings of democracy [5]. Lippmann, one of the founders of communication science, published a book entitled Public Opinion, in which public opinion was defined as an organic product formed in the process of community discussion [6]. He expounded on the internal and external factors in the formation of public opinions. His book can be considered the beginning of public opinion analysis. In general, existing investigations into public opinion generally began with data collection based on survey polls and then analyzed the data using traditional qualitative and quantitative methods [7,8]. A standard survey poll is generated through two processes of selecting representative samples and surveying the samples about their opinions [5]. However, some scholars argue that a survey poll is more likely a way of representing a small group of individuals’ viewpoints rather than public opinion or social opinion [9,10]. In addition, the effects of the hierarchical nature of society on the role of elites in the formation of opinions are considerable in the survey process [11]. Finally, a general research paradigm of survey polls has been built in which the opinions of a few subjectively selected individuals are used to measure the public as a whole. This paradigm largely restricts the publicness of public opinion by representing it based on private and subjective settings. Moreover, due to the time required for and high capital costs involved in data collection for survey polls, data quantity is usually limited, restricting the representativeness of the findings [12] to a large extent. In recent years, with the popularity of cell phones and internet use, cell phone and web polling have emerged. These new forms of opinion collection approaches can enlarge the size of the data set collected, expand sample diversity, reduce associated costs, and accelerate the speed of data collection [13]. Nevertheless, the disadvantages of representative sampling and subjectivity in sample selection remain. Reliability and accuracy problems are also frequently questioned [5].
Social media platforms provide a new way of representing and measuring public opinions. There has been a significant increase in the adoption and use of social media by both the general public and particular subpopulations, such as government sectors, enterprises, and celebrities [14]. In past decades, social media-based public opinion (SMPO) analysis has been conducted in various fields, including social science [15], politics [16], education [17], medical science [18,19], marketing [20], transportation [21], finance [22], knowledge sharing [23], and disaster management [24,25], showing high interest and considerable effectiveness. A specific form in which users both access and share opinions and perceptions is showing a greater transition in social media platforms than perhaps at any previous point in history. A huge amount of opinion data is freely available to researchers, and it shows a more comprehensive picture with higher cost efficiency. Using social media as a data source for public opinion collection can subvert some of the underlying methodological limitations of traditional surveys [10,26]. For example, representative sampling [27,28], the hierarchical nature of opinion formation [5], and difficulties in obtaining time-series data [29] can be eliminated by using social media. Anstead and O'Loughlin [16] suggested that a public and collective stage has been formed in social media mainly because of its conversational nature. McGregor [5] found that social media offers a more temporal-sensitive channel for obtaining and gauging public sentiment toward particular policies and events as the opinions posted when a policy is released and an event occurs are recorded on platforms and cannot be changed. Salleh [30] suggested that social media can not only show a better understanding of public perception in a more scientific manner but can also be helpful in forecasting future political trends and shaping society's worldview. These advantages motivate scholars to use social media as a data source instead of survey polls.
However, despite the advantages brought about by the use of social media in collecting public opinions, certain problems should be taken into consideration, such as difficulties in guaranteeing the relativity of the data, barriers to sharing information honestly and openly, and the reliability and validity of data reprocessing. Although previous studies have more or less proposed some valuable findings, there is still a lack of systematic research on the challenges in the use of social media for public opinion analysis. Therefore, this paper aims to identify the main challenges in SMPO analysis by reviewing existing studies and proposing some practical recommendations.
The research questions are as follows:
- (i)
What is the current state of research on SMPO analysis?
- (ii)
What are the key challenges identified in previous studies?
- (iii)
What are the future perspectives of SMPO analysis?
We answer these questions through a literature review in which we investigate how social media data are used in public opinion analysis with the aim of proposing a framework and some practical suggestions that can guide and help scholars conduct SMPO analysis in a more standardized and comprehensive manner. For this purpose, the study presents a systematic review of the current studies on SMPO analysis by analyzing 54 papers. We identify the motivation for the adoption of social media in public opinion analysis and summarize the existing primary challenges from the perspectives of data collection, data quality, data mining, and ethical considerations. This review also suggests some potential areas where future research can improve upon the significance of the study.
Section snippets
Methodology
To gain a comprehensive understanding of the way in which social media contributes to public opinion analysis, a systematic review of the related literature was carried out in this study, enabling us to identify the existing challenges in this application. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to conduct this review [31]. A qualitative meta-synthesis of the set of publications was conducted to answer the research questions [32], which
Characteristics of the studies
This section presents the following characteristics of the studies reviewed: date of publication, keywords, data source, and design framework for SMPO analysis.
Challenges in and recommendations on social media adoption in public opinion analysis
Based on the reviewed studies, several challenges in SMPO analysis are proposed in this section by considering data collection, data quality, data mining, and ethical considerations.
Conclusion
SMPO analysis is expected to become a central branch of research in future studies as it provides a new way for scholars to better and more comprehensively understand public perceptions on particular issues. This study conducted a systematic review of the corpus of literature on SMPO analysis. On the basis of the reviewed studies, we summarized a general framework for SMPO analysis and identified some challenges in this field by considering data collection, data quality, data mining, and
Declaration of conflicting interests
The authors declare no conflict of interest.
Ethic consideration
This study was performed in accordance with the ethical guidelines from the Ethics Committee of Beijing University of Technology. All subjects gave their written informed consent in accordance with the Declaration of Helsinki. The protocol was approved by the Ethics Committee of Beijing University of Technology.
Acknowledgement
This research was supported by the National Natural Science Foundation of China [grant number 71904010], the Fundamental Research Funds for the Central Universities [CUC210C002], and the Funding Project of Interdisciplinary Research Institute of Beijing University of Technology [grant number 2021110115].
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