User occupation refers to the professional position of a user in real world. It is very helpful for a number of applications, e.g., personalized recommendation and targeted advertising. However, because of the risk of privacy leaks, many users do not provide their occupation information on microblogging platforms. This makes it hard to detect user occupations on microblogging platforms. In this paper, we conduct an experimental study on this issue. Particularly, we propose an experimental framework of detecting user occupations on microblogging platforms. We first implement a number of classification models and devise various sets of features for user occupation detection. Then, we propose to construct an occupation-oriented lexicon, which is collected by an iterative extension algorithm considering semantic similarity and importance between words. We combine the lexicon with the word embedding approach to detect user occupations. We conduct comprehensive experiments and present a set of experimental results. The results show that the lexicon-based word embedding method achieves higher accuracy compared with traditional feature-base classification models.

Detecting User Occupations on Microblogging Platforms: An Experimental Study

PurposeFull text of a document is a rich source of information that can be used to provide meaningful topics. The purpose of this paper is to demonstrate how to use citation context (CC) in the full text to identify the cited topics and citing topics efficiently and effectively by employing automatic text analysis algorithms.Design/methodology/approachThe authors present two novel topic models, Citation-Context-LDA (CC-LDA) and Citation-Context-Reference-LDA (CCRef-LDA). CC is leveraged to extract the citing text from the full text, which makes it possible to discover topics with accuracy. CC-LDA incorporates CC, citing text, and their latent relationship, while CCRef-LDA incorporates CC, citing text, their latent relationship and reference information in CC. Collapsed Gibbs sampling is used to achieve an approximate estimation. The capacity of CC-LDA to simultaneously learn cited topics and citing topics together with their links is investigated. Moreover, a topic influence measure method based on CC-LDA is proposed and applied to create links between the two-level topics. In addition, the capacity of CCRef-LDA to discover topic influential references is also investigated.FindingsThe results indicate CC-LDA and CCRef-LDA achieve improved or comparable performance in terms of both perplexity and symmetric Kullback–Leibler (sKL) divergence. Moreover, CC-LDA is effective in discovering the cited topics and citing topics with topic influence, and CCRef-LDA is able to find the cited topic influential references.Originality/valueThe automatic method provides novel knowledge for cited topics and citing topics discovery. Topic influence learnt by our model can link two-level topics and create a semantic topic network. The method can also use topic specificity as a feature to rank references.

Citation context-based topic models: discovering cited and citing topics from full text

Purpose: Relevance ranking algorithms rank retrieved documents based on the degrees of topical similarity (relevance) between search queries and documents. This paper aims to introduce a new relevance ranking method combining a probabilistic topic modeling algorithm with the “pennant retrieval” method using citation data. Data and Method: We applied this method to the iSearch corpus consisting of c. 435,000 physics papers. We first ran the topic modeling algorithm on titles and summaries of all papers for 65 search queries and obtained the relevance ranking lists. We then used the pennant retrieval to fuse the citation data with the existing relevance rankings, thereby incrementally refining the results. The outcome produced better relevance rankings with papers covering various aspects of the topic searched as well as the more marginal ones. The Maximal Marginal Relevance (MMR) algorithm was used to evaluate the retrieval performance of the proposed method by finding out its effect on relevance ranking algorithms that we used. Findings: Findings suggest that the terms used in different contexts in the papers might sometimes be overlooked by the topic modeling algorithm. Yet, the fusion of citation data to relevance ranking lists provides additional contextual information, thereby further enriching the results with diverse (interdisciplinary) papers of higher relevance. Moreover, results can easily be re-ranked and personalized. Implications: We argue that once it is tested on dynamic corpora for computational load, robustness, replicability, and scalability, the proposed method can in time be used in both local and international information systems such as TR-Dizin, Web of Science, and Scopus. Originality: The proposed method is, as far as we know, the first one that shows that relevance rankings produced with a topic modeling algorithm can be incrementally refined using pennant retrieval techniques based on citation data.

Incremental Refinement of Relevance Rankings: Introducing a New Method Supported with Pennant Retrieval

With the development of scientific research, scientific publications are valuable resources for new-comers in the research field. But massive scientific publications make it a challenge for researchers diving into a new research field. As a good practice to this problem, topics are put forward to organize publications. In this paper, we propose two modified LDA topic models as solutions to topic analysis and influential paper discovery on scientific publications, cc-LDA and cp-LDA. Compared to state-of-the-art researches on LDA, we incorporate citation information including its occurrence times and occurrence position into our models. Model cc-LDA integrates paper content and citation occurrence into LDA model, while cp-LDA considers both occurrence and position of citations. Both models can not only find topics in the form of citation distribution, but also help discover influential papers under certain topics. Furthermore, both models can extract more representative vectors for papers, which achieve good performance in subsequent clustering.

Topic Analysis and Influential Paper Discovery on Scientific Publications.

User age information plays a crucial role in many real applications such as precise marketing, directional promotion and personalized recommendation. In this paper, we focus on predicting user age range in Sina Weibo. To protect user privacy, we only have user basic profile information and user published messages (tweets), which are all mapped to integers. From these meaningless integers, we have to seek out underlying features or structures. Through analysis, we extract significant features related to age. In order to evaluate the correlation between user basic information and age ranges, we choose mutual information as measurement. To handle the problem of high dimensions and data sparsity caused by traditional word vector model of tweet contents, we propose aggregated tweet features corresponding to different age ranges. Using these features, we compared many classification algorithms. Finally, the model based on decision tree can achieve best prediction accuracy up to 83%.

Ye Li

Papers

Topic Analysis and Influential Paper Discovery on Scientific Publications.

Predicting Microblog User’s Age Based on Text Information

Topic Analysis and Influential Paper Discovery on Scientific Publications