Social web or Web 2.0 has gain popularity since last decade due to its valuable services such as social networking, blogs, online forums, through which users can easily produce and consume information. Online discussion forums are an emerging service of social web, provide an excellent opportunity for knowledge exchange and sharing of ideas. In online forums, collaborations occur when questioning-answering take place among online forum members. Expert finding in online discussion forums, such as BBC, StackOverflow, is a specialized problem of information retrieval. Previously, the expert finding approaches in online forums were based on content and link based features. The link based expert ranking techniques are based on users’ social network authority and can be measured through link analysis techniques such as PageRank and HITS. Content based techniques utilize the answers content to measure user’s reputation or expertise. Posts contents quality can be measured through textual and non-textual features. Textual similarity is measured through standard similarity techniques such as cosine and semantic similarity. Non-textual features include post length, position, references and sentiments etc. Users expertise are measured through their self-reputation scores, however, users performance is not evaluated on the basis of their neighbors’ or co-existing participants’ reputation scores. Moreover, important features such as user’s activity, participation strength, discussion quality and consistent performance have not been utilized for expert finding problem. Thread ranking is another specialized problem of information retrieval in online discussion forums with the aim of finding relevant and quality threads for a given query. Thread ranking problem is addressed through structure and content-similarity features, however features such as semantic similarity, participants’ reputation and thread structure have not been utilized. In this research work, we propose improved expert finding techniques for both rated and non-rated discussion forums such as BBC and StackOverflow. In case of non-rated forums like BBC, we measure the users’ expertise through their co-existing users’ reputation. Users who answer together in multiple threads are termed as Co-existing users. For expert finding in rated-forums like StackOverflow, our techniques consider the element of consistent performance of a user. Reputation features are derived from StackOverflow dataset which are based on voter reputation, vote ratio and tags popularity. We have validated our both expert ranking techniques (for rated and non-rated forums) against a link based expert finding technique and achieved quality results. Lastly, we have addressed the thread ranking problem in BBC forums. Threads quality has been measured through structure, content quality and participant reputation. Experiments on BBC forum dataset show that our thread ranking technique outperforms the baseline technique.