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Rumor is a collective attempt to interpret a vague but attractive situation by using the power of words. In social networks, false-rumors may have significantly different contextual characteristics from true-rumors at lexical, syntactic, semantic levels. Therefore, this study presents the BERT-SAWS semi-supervised learning model for early verification of Persian rumor by investigating content-based and context features at three views: Contextual Word Embeddings (CWE), speech act, and Writing Style (WS). This model is built by loading pre-trained Bidirectional Encoder Representations from Transformers (BERT) as an unsupervised language representation, fine-tuning it using a small Persian rumor dataset, and combining with a supervised learning model to provide an enriched text representation of the content of the rumor. This text representation enables the model to have a better comprehending of the …