Twitter is one of the most popular sources of information available on the internet. Thus, many studies have proposed tools and models to analyze the credibility of the information shared. The credibility analysis on Twitter is generally supported by measures that consider the text, the user, and the social impact of text and user. More recently, identifying the topic of tweets is becoming an interesting aspect for many applications that analyze Twitter as a source of information, for example, to detect trends, to filter or classify tweets, to identify fake news, or even to measure a tweet’s credibility. In most of these cases, the hashtags represent important elements to consider to identify the topics. In a previous work, we presented a credibility model based on text, user, and social credibility measures, and a framework called T-CREo, implemented as an extension of Google Chrome. In this paper, we propose an extension of our previous credibility model by integrating the detection of the topic in the tweet and calculating the topic credibility measure by considering hashtags. To do so, we evaluate and compare different topic detection algorithms, to finally integrate in our framework T-CREo, the one with better results. To evaluate the performance improvement of our extended credibility model and show the impact of hashtags, we performed experiments in the context of fake news detection using the PHEME dataset. Results demonstrate an improvement in our extended credibility model with respect to the original one, with up to 3.04% F1 score when applying our approach to the whole PHEME dataset and up to 9.60% F1 score when only considering tweets that contain hashtags from PHEME dataset, demonstrating the impact of hashtags in the topic detection process.