Public opinion research in the Middle Eastern context has seen a proliferation of data research that aims to track changes in the state of Arab attitudes. Yet, it remains unclear what is being measured and how representative the data is. This article aims to address this question by reviewing the extent to which and how the existing literature has addressed the issues of representativeness of Twitter communities and the validity of opinion measures derived from sentiment analysis in the Middle Eastern context. While some studies aim no further than to gauge the dynamics of Twitter debates, many others seek to generalize the larger public opinion trends. This raises questions about how representative Twitter users are of the general public, how asymmetric social media use is, and how participation routines vary. Furthermore, to what extent can sentiment analysis translate individual tweets into meaningful measures of expressed opinion, and how sensitive are means of aggregating opinions from sentiment analysis to variation in terms of tone and amount of text within and across individuals? Following a comprehensive review of existing research in the context of the Middle East, this article also aims to derive a clearer understanding of how collective Twitter opinion relates to public opinion and make some suggestions of ways to design sampling and coding procedures, as well as validation exercises to address measurement bias and error.

在中东范围内的民意研究已经看到了旨在追踪阿拉伯人态度状态变化的数据研究的泛滥。然而，目前尚不清楚正在测量什么以及数据的代表性。本文旨在通过回顾现有文献在多大程度上以及如何解决Twitter社区的代表性问题以及在中东背景下从情感分析得出的意见措施的有效性来解决这个问题。尽管有些研究的目的只是为了评估Twitter辩论的动态，但其他一些研究则试图概括更大的舆论趋势。这就引发了关于代表性的Twitter用户如何成为普通公众，社交媒体使用不对称以及参与程序如何变化的问题。此外，情感分析可以在多大程度上将个人推文转化为表达意见的有意义的量度，以及将意见从情感分析汇总到个人内部和个人之间的语调和文本量变化的敏感性如何？在对中东背景下的现有研究进行全面回顾之后，本文还旨在更清晰地了解Twitter的集体意见与公众舆论的关系，并对设计抽样和编码程序以及验证的方式提出一些建议。练习解决测量偏差和误差。以及从情感分析收集意见对个人内部和个人之间的语调和文本量变化的敏感性如何？在对中东背景下的现有研究进行全面回顾之后，本文还旨在更清晰地了解Twitter的集体意见与公众舆论的关系，并对设计抽样和编码程序以及验证的方式提出一些建议。练习解决测量偏差和误差。以及从情感分析收集意见对个人内部和个人之间的语调和文本量变化的敏感性如何？在对中东背景下的现有研究进行全面回顾之后，本文还旨在更清晰地了解Twitter的集体意见与公众舆论的关系，并对设计抽样和编码程序以及验证的方式提出一些建议。练习解决测量偏差和误差。