Due to name abbreviations, identical names, name misspellings, and pseudonyms in publications or bibliographies (citations), an author may have multiple names and multiple authors may share the same name. Such name ambiguity affects the performance of document retrieval, web search, database integration, and may cause improper attribution to authors. This paper investigates two supervised learning approaches to disambiguate authors in the citations 1 . One approach uses the naive Bayes probability model, a generative model; the other uses Support Vector Machines(SVMs) [39] and the vector space representation of citations, a discriminative model. Both approaches utilize three types of citation attributes: co-author names, the title of the paper , and the title of the journal or proceeding. We illustrate these two approaches on two types of data, one collected from the web, mainly publication lists from homepages, the other collected from the DBLP citation databases.

Micro-blogging services have become indispensable communication tools for online users for disseminating breaking news, eyewitness accounts, individual expression, and protest groups. Recently, Twitter, along with other online social networking services such as Foursquare, Gowalla, Facebook and Yelp, have started supporting location services in their messages, either explicitly, by letting users choose their places, or implicitly, by enabling geo-tagging, which is to associate messages with latitudes and longitudes. This functionality allows researchers to address an exciting set of questions: 1) How is information created and shared across geographical locations, 2) How do spatial and linguistic characteristics of people vary across regions, and 3) How to model human mobility. Although many attempts have been made for tackling these problems, previous methods are either complicated to be implemented or oversimplified that cannot yield reasonable performance. It is a challenge task to discover topics and identify users' interests from these geo-tagged messages due to the sheer amount of data and diversity of language variations used on these location sharing services. In this paper we focus on Twitter and present an algorithm by modeling diversity in tweets based on topical diversity, geographical diversity, and an interest distribution of the user. Furthermore, we take the Markovian nature of a user's location into account. Our model exploits sparse factorial coding of the attributes, thus allowing us to deal with a large and diverse set of covariates efficiently. Our approach is vital for applications such as user profiling, content recommendation and topic tracking. We show high accuracy in location estimation based on our model. Moreover, the algorithm identifies interesting topics based on location and language.

In this paper, we introduce simple graph clustering methods based on minimum cuts within the graph. The clustering methods are general enough to apply to any kind of graph but are well suited for graphs where the link structure implies a notion of reference, similarity, or endorsement, such as web and citation graphs. We show that the quality of the produced clusters is bounded by strong minimum cut and expansion criteria. We also develop a framework for hierarchical clustering and present applications to real-world data. We conclude that the clustering algorithms satisfy strong theoretical criteria and perform well in practice.

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Clickbaits are articles with misleading titles, exaggerating the content on the landing page. Their goal is to entice users to click on the title in order to monetize the landing page. The content on the landing page is usually of low quality. Their presence in user homepage stream of news aggregator sites (e.g., Yahoo news, Google news) may adversely impact user experience. Hence, it is important to identify and demote or block them on homepages. In this paper, we present a machine-learning model to detect clickbaits. We use a variety of features and show that the degree of informality of a webpage (as measured by different metrics) is a strong indicator of it being a clickbait. We conduct extensive experiments to evaluate our approach and analyze properties of clickbait and non-clickbait articles. Our model achieves high performance (74.9% F-1 score) in predicting clickbaits.