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The purpose of this paper is to advance a conceptual analytical framework to help explain employment regulation as a dynamic process shaped by institutions and actors. The paper builds on and advances regulatory space theory.

The paper analyses the literature on regulatory theory and engages with its theoretical development.

The paper advances the case for a broader and more inclusive regulatory approach to better capture the complex reality of employment regulation. Further, the paper engages in debates about the complexity of employment regulation by adopting a multi-level perspective.

The research proposes an analytical framework and invites future empirical investigation.

The paper contends that existing literature affords too much attention to a (false) regulation vs deregulation dichotomy, with insufficient analysis of other “spaces” in which labour policy and regulation are formed and re-formed. In particular, the proposed framework analyses four different regulatory dimensions, combining the legal aspects of regulation with self-regulatory dimensions of employment regulation.

Social media is characterized by its volume, its speed of generation and its easy and open access; all this making it an important source of information that provides valuable insights. Content characteristics such as valence and emotions play an important role in the diffusion of information; in fact, emotions can shape virality of topics in social media. The purpose of this research is to fill the gap in event detection applied on online content by incorporating sentiment, more specifically strong sentiment, as main attribute in identifying relevant content.

The study proposes a methodology based on strong sentiment classification using machine learning and an advanced scoring technique.

The results show the following key findings: the proposed methodology is able to automatically capture trending topics and achieve better classification compared to state-of-the-art topic detection algorithms. In addition, the methodology is not context specific; it is able to successfully identify important events from various datasets within the context of politics, rallies, various news and real tragedies.

This study fills the gap of topic detection applied on online content by building on the assumption that important events trigger strong sentiment among the society. In addition, classic topic detection algorithms require tuning in terms of number of topics to search for. This methodology involves scoring the posts and, thus, does not require limiting the number topics; it also allows ordering the topics by relevance based on the value of the score.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-12-2019-0373