Exploring user stances and attitudes is beneficial to a number of Web related research and applications, especially in social media platforms such as Twitter. Stance detection in Twitter aims at identifying the stance expressed in a tweet towards a given target (e.g., a government policy). A key challenge of this task is that a tweet may not explicitly express opinion about the target. To effectively detect user stances implied in tweets, target content information plays an important role. In previous studies, conventional feature-based methods often ignore target content. Although more recent neural network-based methods attempt to integrate target information using attention mechanism, the performance improvement is rather limited due to the underuse of this information. To address this issue, we propose an end-to- end neural model, TGMN-CR, which makes better use of target content information. Specifically, our model first learns conditional tweet representation with respect to specific target. It then employs a target-guided iterative process to extract crucial stance-indicative clues via multiple interactions between target and tweet words. Experimental results on SemEval-2016 Task 6.A Twitter Stance Detection dataset show that our proposed method outperforms the state-of-the-art alternative methods, and substantially outperforms the comparative methods when a tweet does not explicitly express opinion about the given target.