Further Work

This section discusses potential short-term and long-term further work that could be undertaken for the research in this PhD thesis.

7.3.1.

Short-Term Future Work

Following on the research carried out in this PhD thesis, this sub-section discusses potential work that could be undertaken in the short-term future. The LSR-FGM considers the language expertise inference of SNS users from their social profiles. In terms of the future work on the basis of the proposed LSR-FGM, one possible route is to exploit more external resources to enhance the user profiles. Social profiles initially provided by the users are usually incomplete, thus, it is important to associate them with more related information that could help language inference, such as the locations they have worked. Another possible route is the design of more advanced features that may be related to the user’s language expertise. Although the sentiment analysis based approach has been shown as an effective feature weighting scheme for user expertise inference, future work could also involve exploring other weighting schemes that can better represent the user’s expertise, such as the use of the user’s hedging language [Ma13]. In terms of the general topic of expertise inference of cold start users in SNSs, future work may still focus on the use of the user’s social profiles, but with the aim to infer other expertise information about the user. For example, the studying and working experiences stated in the user’s profile

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could also indicate what professional skills the user may have. Apart from the use of static profiles, other limited interactions cold start users have in a SNS should also be explored, such as the list of users the user follows/connects to. In this case, studies are expected to identify their connections with the user’s expertise information.

Another focus of this PhD research is the topical expertise inference of active Twitter users. On the top of the two learning models proposed, one possible future route is to design more effective approaches of constructing relations between expertise topics. It was shown that the relations between expertise topics play an important role in the inference of the user’s topical expertise, however, the approach applied in the SeTRL model for constructing the relation is relatively simple, i.e. based on their co-occurrence frequencies. Thus, it may be possible to exploit more resources, or conduct user studies, to obtain the relations that can better represent the intrinsic connections between expertise topics of an individual. Another possible route of the future work is to investigate the impact of inference models on different expertise topics. The design of the learning models in this PhD thesis targets for better overall inference performance on all the expertise topics, so its performance on different topics may be inconsistent. In application scenarios, they may need to improve its performance on particular topic(s), while maintaining its overall performance. Thus, there is a need for the design of topic-aware learning models for expertise inference.

The last focus of this PhD research is the application of the inferred user expertise using the proposed modelling approaches. As there is a lack of multilingual content on Quora, the inferred language expertise was not applied in the case study research. Thus, the construction of related datasets with multilingual content or the study on the application of the inferred language information in other potential application scenarios will be one possible route of future work. In addition, the case study research validated the usefulness of the inferred user expertise and did not explore to what extent the inferred expertise can facilitate the answerer finding service on a CQA site. Thus, another possible route of future work is to design approaches that can more effectively utilise the inferred user expertise on different application scenarios. Furthermore, it is also noted that the inactivity of cold start users in a CQA site may be part of the classic online community lurker behavior. In this case, effective modelling of these users will not help to encourage them answer new questions. To address this type of problem, the enhanced user profile from their SNS content could be used to infer motivational factors, such as targeted prizes

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and direct attention from the community moderators, to help engage them in question answering.

7.3.2.

Long-Term Future Work

This sub-section discusses potential research directions this PhD research could lead to in the long-term future. This PhD research discovered a variety of user expertise-related factors, such as tweet sentiment and topic relatedness, and incorporated them in novel user expertise modelling approaches for effective user expertise inference in the SNS setting. It is a straightforward future route to keep exploring other factors that are associated with the user’s expertise information and optimizing expertise modelling approaches accordingly. However, the limitation in accessing the SNS data would prevent the development of this direction of research. It was noted that the exportation of this PhD research was limited to the public LinkedIn profiles and public data of the Twitter users. Therefore, to continue on this line of research in the long term, expanding access or gaining full access to data in SNS platforms, such as Twitter or Facebook, is required. This PhD research focused on the utilization of the publicly available user data and did not involve privacy related issues. However, as indicated in this thesis, the proposed expertise modelling approaches can be readily applied to other SNS platforms, which would bring in concerns on the use of the privacy-sensitive user data in platforms such as Facebook. This will lead to a new line of research, i.e. privacy-aware user expertise modelling in SNSs. Future research could involve designing effective user expertise modelling approaches under the context of controlled user data access or effective privacy protection strategies in user expertise modelling.

Researchers in academia often suffer the problem of the lack of real-world experimental data. They are usually at a disadvantage when performing research that relies on real- world user data, compared with the research teams within that platform, e.g. Twitter and Facebook. Under this background, this leads this PhD research to another line of research, which focuses on user expertise modelling for targeted groups of users through the use of their SNS content and local documents, such as employees within large multinational companies or researchers within large research centres. Academic researchers are usually affiliated with certain research groups or in collaboration with some industry companies. This allows them to have access to the data of certain groups of people, which could include their local documents, e.g. project files and publications, and SNS content, e.g. Facebook updates. Although extensive research has been conducted to study the problem

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of user expertise modelling within an organization by using their local documents (reviewed in Section 2.2), the research question: to what extent can the user’s social content and local documents within an organization be jointly exploited to model their expertise, remains unanswered. Therefore, future research could focus on the design of effective expertise modelling approaches through the combinational use of the user’s various social content and local documents. Furthermore, based on the constructed user expertise model, future research could also involve the design of effective user search approaches or user recommendation approaches, in order to serve targeted applications within the organization, such as personalized talent search, task match.