The studies of the network formed by social insects require the motion analysis of their interactions and movements in videos over an extended period of time. Automated detection is an important field of interest because it enables the motion analysis in large-scale experiments. When an automated detection method is applied to various insect types, the training task often involves the collection of a large number of labels provided by human experts. To save the experts' time and effort, unlabeled data have been recently employed to supplement the training. In this paper, we utilize the spatiotemporal connectivity of the unlabeled data to regulate the training of a detector on a new insect type. Our key contribution is integrating the spatiotemporal connectivity among the unlabeled samples to determine the weighting scheme of the existing classifiers from multiple sources. The evaluation on 3 data sets of social insects consisting of 6,000 samples demonstrates that a detector trained using our method can achieve comparable performance to previous approaches while reducing the training labels up to 16 times. Since the proposed method is based on regularizing the unlabeled samples based on their spatiotemporal connectivity, we refer to it as the Spatio-Temporally Regularized Adaptive Learning (STRAL).