Time is a fundamental concept in data processing. The growth of social media (SM) data and the rise of the Internet of Things (IoT) underscores the necessity for studying temporal data on the Web. However, accessing realistic temporal data poses significant challenges across data collection, knowledge representation, and real-time provisioning, with no comprehensive solution available yet. To tackle these challenges, we introduce GenACT, a novel data generator rooted in the dynamics of Academic Conference Tweets (ACT), which serves as an ideal domain for eliciting application scenarios spanning temporality, dynamicity, and timeliness. The foundation of GenACT is a domain-specific ontology crafted to conceptualize tweets around an Academic Conference Event (ACE) realistically. The ACE ontology is available in all four OWL 2 profiles. Additionally, RDF instantiation allows for real-time simulation of ongoing academic discussions on Twitter. GenACT stands out for its ability to configure different data segments using SPARQL-based partitioning strategies. This versatility makes it adaptable to various analytical tasks, enabling researchers to focus on specific aspects of the data for their studies. GenACT is designed to seamlessly provide temporal and static data in a streaming format, tailored specifically for applications in studying knowledge graph evolution, temporal reasoning, and stream reasoning. The ontology, code, and documentation are available under the Apache 2.0 License at https://github.com/kracr/temporal-data-generator .