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This chapter discusses the development of public opinion polls in Malaysia, especially those related to politics and elections in the state. It traces the early attempts at public opinion polls and their relatively recent development in estimating electoral outcomes in Malaysia. In analysing the efforts, the chapter relates the conduct of opinion polls with changing trends in Malaysia’s elections, particularly the shift from a dominant party system to one of two-coalition systems since the 12th general elections (GE12) in 2008. The chapter also discusses the role of the various actors who are involved in opinion polling, and the usual contents in such polls. It evaluates the possibility and difficulty of the polls in estimating outcomes of elections. In view of the new norms developing since the spread of COVID-19 in 2020, the chapter also explores the challenges and opportunities of public opinion polls in articulating public sentiment.

Upholds the equivalence between the sociological notion of reflexivity and the idea of self‐reference, which epistemologically and structurally distinguish those complex systems where humans participate as elements. Once there is a conceptual bridge between sociology and cybernetics, the reflexivity processes are analysed, and the expression feed‐before is proposed for the corresponding circuits, as they operate after the input, but before the output characteristic of the other complex systems is produced. After distinguishing between micro‐ and macro‐reflexivity, studies mutual connections and submits graphic designs of both types of circuit. Also briefly analyses the tempo of social reflexivity, while deducing the consequences regarding the predictability of social dynamics.

This article is the third in a trilogy of articles that discuss the professionalism (or deprofessionalism) of the accounting profession. The first examines the slow uphill climb of accounting and auditing practice to the level of being recognized as a highly trusted profession. The second examines the stagnation in professionalism leading to deprofessionalization of the accounting profession. This third article looks at the resulting directionless efforts of accounting and auditing firms in the wake of major deprofessionalization events. The interest in this study is the time period immediately following the passage of the Sarbanes–Oxley Act (SOX) of 2002 which is described in this paper as the “Post-SOX” history of public accountancy in the United States. During this time period, nearly equally mixed activities of professionalism and deprofessionalism have resulted in a status quo with directionless efforts doing little if anything to reverse decline in professionalism. Public accountants continued to experience conflict with the Securities and Exchange Commission (SEC) over independence rules. The large Certified Public Accountant firms generated controversies and squabbles concerning “auditing and consulting,” while at the same time they faced questions regarding the marketing and selling of aggressive tax shelters. In addition, most of the self-regulating aspects of the profession declined dramatically following passage of SOX. While initially both tax fees and audit fees of CPA firms increased during this time period, concerns are again arising as the large CPA firms more recently have renewed the emphasis on advisory services. While revenues have both increased and changed in composition during the post-SOX era, public opinion has maintained a status quo. The post-SOX era has also seen a weakening in the Code of Conduct, providing more liberties for CPAs to maximize self-interest. Meanwhile, the PCAOB faced constitutional challenges, while at the same time the AICPA experienced strong divisions in its membership. To provide some sense to these directionless efforts, this study, similar to the prior two articles in this trilogy, concludes with a summary analysis based on the nine SOCRECELIST criteria, and the question whether public accountants have learned their history lesson.

The purpose of this paper is to suggest public service corporations as a new means of helping to finance comprehensive public colleges and universities based on a well‐documented assumption that the current shared responsibility for financing public colleges is broken.

The paper focuses on financing comprehensive public colleges and universities, and explicitly does not focus on research, community, or proprietary institutions. The paper draws heavily from national data and literature on college finance and productivity, and uses New Jersey's state colleges and universities as primary examples.

The paper asserts that a new funding rationale for public colleges is imperative or these institutions will fail the principal mission of broad access for middle‐income students. Citing examples from New Jersey and other states, and drawing on work of other policy analysts, the paper proposes creation of new public service corporations not only as a means of generating new revenue to replace diminished state investment, but also as a means of enhancing transparency, accountability and public trust. The paper discusses explicit purposes and measurable benefits of the public service corporation.

The paper is written by two higher education policy practitioners with a combined 40 years executive experience in higher education law and policy at the state and national levels. They have been a leading voice for policy innovation in New Jersey. The paper has significant value for college presidents, trustees, governors, legislators, and policy analysts.

Do public opinion and political sentiments expressed on Twitter during election campaign have a meaning and message? Are they inferential, that is, can they be used to estimate the political mood prevailing among the masses? Can they also be used to reliably predict the election outcome? To answer these in the Indian context, the 2014 general election was chosen.

Tweets posted on the leading parties during the voting and crucial campaign periods were mined and manual sentiment analysis was performed on them.

A strong and positive correlation was observed between the political sentiments expressed on Twitter and election results. Further, the Time Periods during which the tweets were mined were found to have a moderating effect on this relationship.

This study showed that the month preceding the voting period was the best to predict the vote share with Twitter data – with 83.9% accuracy.

Twitter has become an important public communication tool in India, and as the study results reinstate, it is an ideal research tool to gauge public opinion.

Social media provide an impressive amount of data about users and their interactions, thereby offering computer and social scientists, economists, and statisticians – among others – new opportunities for research. Arguably, one of the most interesting lines of work is that of predicting future events and developments from social media data. However, current work is fragmented and lacks of widely accepted evaluation approaches. Moreover, since the first techniques emerged rather recently, little is known about their overall potential, limitations and general applicability to different domains. Therefore, better understanding the predictive power and limitations of social media is of utmost importance.

Different types of forecasting models and their adaptation to the special circumstances of social media are analyzed and the most representative research conducted up to date is surveyed. Presentations of current research on techniques, methods, and empirical studies aimed at the prediction of future or current events from social media data are provided.

A taxonomy of prediction models is introduced, along with their relative advantages and the particular scenarios where they have been applied to. The main areas of prediction that have attracted research so far are described, and the main contributions made by the papers in this special issue are summarized. Finally, it is argued that statistical models seem to be the most fruitful approach to apply to make predictions from social media data.

This special issue raises important questions to be addressed in the field of social media-based prediction and forecasting, fills some gaps in current research, and outlines future lines of work.