Discovery of topic flows of authors

As the number of Web documents is increasing exponentially, it becomes important to develop methods to extract useful information or knowledge from the documents. There are many knowledge discovery problems, one of which is the discovery of academic research interests. The discovery of research interests may give an insight into research trends according to a particular period and further may help researchers to make wise decisions for their future research topics.

It is important to note the difference between the discovery of academic interests and identifying experts [7]. The task of discovery of academic interests is to find people who write about particular topics, and the identifying experts task is to find the most skilled people for particular research topics. The discovery of academic interests, therefore, assumes that an author writes much about a particular topic when the author is interested in the topic. If the academic interests are plotted along the time sequence, then it will be a trend of academic interests. The trend of academic interests can be used to develop useful services such as finding similar authors (potential co-authors) to a particular author, finding top researchers about a specific research domain, or a graph of research trends. For example, the research trend graph is provided by Microsoft academic research, as shown in Fig. 1. When we pick the research topic Networks and Communications, then the dominant author list is shown in the bottom. By navigating such research trend graph, we can get useful information about ‘Which research area is hot, nowadays?’, ‘Who is the expert in this area?’, or ‘Will this research area rise again?’.

There are three practical factors to consider for the task of discovering research interests from academic Web documents. First, the academic documents typically do not have consistent labels or categories. Although each academic document provides keywords and categories that it belongs to, the keywords and categories are not standardized with other documents, conferences, or journals. The inconsistent keywords and categories make the discovery of academic interests more difficult. Second, it is necessary to capture latent semantic structures (i.e., research topics) that are common across all the documents. Each academic document may contain multiple research topics, so every topic is shared by all the documents. Third, academic Web documents are dynamic, so the research topics rise or fall as time goes by. The time factor conveys temporal patterns of research interests, which is important information.

To address the above factors, clustering techniques have been investigated to overcome this obstacle [15]. In this paper, we adopt a topic modeling approach which is one of such techniques, where each topic is regarded as a specific research domain. When we use a word as a feature, then each document can be represented as a sequence of words. This representation, however, is not practical because the number of possible sequences of unique words grows exponentially as the size of each document increases. Therefore, the sequence of words is usually ignored, and only the word frequencies are utilized, which is called bag-of-words (BOW) method. Although this method misses location information of words, it has shown good performances with various types of data. The topic modeling approach also takes the BOW method. Each topic or cluster obtained from the topic modeling approach conveys latent semantic meaning across the documents. When the number of unique words, called the vocabulary size, is V, then a topic is represented as a V-dimensional vector of weighted unique words. For each topic, each unique word has a weight, and unique words that have greater weights are more representative of the topic. By looking at the top representative words of each topic, human can interpret and give a name to the topic. The sum of word weights in each topic is 1, so a topic is a distribution across the set of unique words. Thus, each topic is a distribution across the set of unique words, and different topics represent different semantic meaning by having different weights of the unique words. For example, if there are only three unique words {music, film, rock} and the top two words of a particular topic is {film, music}, then we can interpret the topic is OST music. If the top two words of another topic are {music, rock}, then we might interpret the topic is rock music. In this paper, we regard each topic as a research domain, so the topic proportion can be interpreted as the interest of the domains. Further, if the topic proportions are listed according to time information, then it will be a temporal pattern or a trend of research interests. Our proposed new topic model captures such temporal pattern of each author from academic Web documents.

The rest of this paper is organized as follows. Section 2 provides preliminary background for understanding the topic modeling approaches and related studies. Section 3 describes our motivation and approach in detail, and Section 4 presents experiments and results. Finally, Sect. 5 brings this paper to conclusions and future work.

The past efforts for discovering research interests can be divided into three categories as described in Daud [6]. First, from a piece of text, research topics are identified by considering predefined features such as sentence length, forensic linguistics, and authorship attributions [12, 12]. The predefined features have tremendous impact on the performance, so these approaches heavily rely on the features. One big limitation of this approach is that it will require huge effort to develop different feature sets for different types of data. Second, from explicit connections between authors (e.g., co-writing), arbitrary relationships between the authors can be extracted in a form of graph structure [11, 21, 30]. This approach usually does not utilize the content of documents, so it is not likely to exactly capture the research interests of the authors. For example, if two authors A and B wrote a paper together, then the two authors might have similar research interests. However, this does not help to answer the question ‘What is the most favorite research topic of A?’. Moreover, this approach is not applicable to the authors who usually write papers alone. Third, from the contents of documents, arbitrary semantic structures can be extracted. As the number of documents without annotations (i.e., unstructured texts) is growing exponentially, it is preferred to take unsupervised methods [5, 23, 31]. Topic modeling is one of such methods, and it captures the latent semantic structures across the documents. Many topic models and related studies have been proposed [19, 26, 28], where they are mainly motivated from the probabilistic latent semantic analysis (PLSA) model [14] or the latent Dirichlet allocation (LDA) model [3]. For instance, some models extract topics from the perspectives of authors Mimno and McCallum [20], Rosen-Zvi et al [24], Steyvers et al [25]. These models commonly assume that authors have topic distributions. In Chang et al [4], Newman et al [22], the topics are extracted from the perspectives of entities, where the entities are commonly supposed to have topic distributions. These models, however, do not consider the time dimension, so they suffer from a topic exchangeability problem. Assume that one uses AT model [24] to identify academic trends of a given author for two years. The AT model simply assumes that each author has a topic distribution which does not have any time information or temporal patterns. Thus, the model should be applied to each year separately. In other words, there will be two AT models for two years, respectively, and there will be two sets of topics \(\phi ^{\mathrm{first\,year}}\) and \(\phi ^{\mathrm{second\,year}}\). It should be noted that the tth topic \(\phi ^{\mathrm{first\,year}}_t\) of the first year will not be equivalent to the tth topic \(\phi ^{\mathrm{second\,year}}_t\) of the second year. In the worst case, the tth topic of the first year may be out of existence in the second year. This inconsistency of topics is called as the exchangeability problem [6].

We propose a new topic model to obtain the research interests of authors without the drawbacks of the exchangeability problem. As each topic model has its unique generative structure that represents its own hypothesis, it will be helpful to discuss the existing salient models related to our proposed model.

In this section, we clarify the definition of the problem to be solved and offer our contributions. Then we describe the new proposed topic model in detail.

The amount of Web documents increases exponentially, which makes it necessary to develop systems or models to automatically capture some latent patterns among the documents. The latent patterns typically change over time, which compose topical flows or trends. In this paper, we propose Author Topic-Flow (ATF) model, which effectively discovers research trends in each author’s point of view. The state-of-the-art model, namely Temporal Author Topic (TAT) model, combines the temporal patterns of topics to compute the research trends of the authors, so we denote it as indirect topic flow. On the other hand, our proposed model has a variable to directly represent the research trends of each author, so we denote it as direct topic flow. That is, it allows each author to directly have a temporal pattern of research interests, while each author in the TAT model has only a topic proportion covering all the time spans. We performed empirical comparisons with the TAT model with a real-world dataset, and we proved that the ATF model is more effective and efficient in capturing the temporal patterns of research interests of authors. We hope that this study would be helpful in some other research areas such as user-adapted Web content recommendation on mobile platform [18] or out-of-domain detection of intelligent dialog systems.