Leonidas Akritidis

Identifying Influential Bloggers: Time Does Matter

Blogs have recently become one of the most favored services on the Web. Many users maintain a blog and write posts to express their opinion, experience and knowledge about a product, an event and every subject of general or specific interest. More users visit blogs to read these posts and comment them. This “participatory journalism” of blogs has such an impact upon the masses that Keller and Berry argued that through blogging “one American in tens tells the other nine how to vote, where to eat and what to buy” [9]. Therefore, a significant issue is how to identify such influential bloggers. This problem is very new and the relevant literature lacks sophisticated solutions, but most importantly these solutions have not taken into account temporal aspects for identifying influential bloggers, even though the time is the most critical aspect of the Blogosphere. This article investigates the issue of identifying influential bloggers by proposing two easily computed blogger ranking methods, which incorporate temporal aspects of the blogging activity. Each method is based on a specific metric to score the blogger’s posts. The first metric, termed MEIBI, takes into consideration the number of the blog post’s inlinks and its comments, along with the publication date of the post. The second metric, MEIBIX, is used to score a blog post according to the number and age of the blog post’s inlinks and its comments. These methods are evaluated against the state-of-the-art influential blogger identification method utilizing data collected from a real-world community blog site. The obtained results attest that the new methods are able to better identify significant temporal patterns in the blogging behaviour

Effective rank aggregation for metasearching

Nowadays, mashup services and especially metasearch engines play an increasingly important role on the Web. Most of users use them directly or indirectly to access and aggregate information from more than one data sources. Similarly to the rest of the search systems, the effectiveness of a metasearch engine is mainly determined by the quality of the results it returns in response to user queries. Since these services do not maintain their own document index, they exploit multiple search engines using a rank aggregation method in order to classify the collected results. However, the rank aggregation methods which have been proposed until now, utilize a very limited set of parameters regarding these results, such as the total number of the exploited resources and the rankings they receive from each individual resource. In this paper we present QuadRank, a new rank aggregation method, which takes into consideration additional information regarding the query terms, the collected results and the data correlated to each of these results (title, textual snippet, URL, individual ranking and others). We have implemented and tested QuadRank in a real-world metasearch engine, QuadSearch, a system developed as a testbed for algorithms related to the wide problem of metasearching. The name QuadSearch is related to the current number of the exploited engines (four). We have exhaustively tested QuadRank for both effectiveness and efficiency in the real-world search environment of QuadSearch and also, using a task from the recent TREC-2009 conference. The results we present in our experiments reveal that in most cases QuadRank outperformed all component engines, another metasearch engine (Dogpile) and two successful rank aggregation methods, Borda Count and the Outranking Approach.

Improving opinionated blog retrieval effectiveness with quality measures and temporal features

The massive acceptance and usage of the blog communities by a significant portion of the Web users has rendered knowledge extraction from blogs a particularly important research field. One of the most interesting related problems is the issue of the opinionated retrieval, that is, the retrieval of blog entries which contain opinions about a topic. There has been a remarkable amount of work towards the improvement of the effectiveness of the opinion retrieval systems. The primary objective of these systems is to retrieve blog posts which are both relevant to a given query and contain opinions, and generate a ranked list of the retrieved documents according to the relevance and opinion scores. Although a wide variety of effective opinion retrieval methods have been proposed, to the best of our knowledge, none of them takes into consideration the issue of the importance of the retrieved opinions. In this work we introduce a ranking model which combines the existing retrieval strategies with query-independent information to enhance the ranking of the opinionated documents. More specifically, our model accounts for the influence of the blogger who authored an opinion, the reputation of the blog site which published a specific blog post, and the impact of the post itself. Furthermore, we expand the current proximity-based opinion scoring strategies by considering the physical locations of the query and opinion terms within a document. We conduct extensive experiments with the TREC Blogs08 dataset which demonstrate that the application of our methods enhances retrieval precision by a significant margin.

Computing scientometrics in large-scale academic search engines with mapreduce

Apart from the well-established facility of searching for research articles, the modern academic search engines also provide information regarding the scientists themselves. Until recently, this information was limited to include the articles each scientist has authored, accompanied by their corresponding citations. Presently, the most popular scientific databases have enriched this information by including scientometrics, that is, metrics which evaluate the research activity of a scientist. Although the computation of scientometrics is relatively easy when dealing with small data sets, in larger scales the problem becomes more challenging since the involved data is huge and cannot be handled efficiently by a single workstation. In this paper we attempt to address this interesting problem by employing MapReduce, a distributed, fault-tolerant framework used to solve problems in large scales without considering complex network programming details. We demonstrate that by setting the problem in a manner that is compatible to MapReduce, we can achieve an effective and scalable solution. We propose four algorithms which exploit the features of the framework and we compare their efficiency by conducting experiments on a large dataset comprised of roughly 1.8 million scientific documents.

Improved retrieval effectiveness by efficient combination of term proximity and zone scoring: A simulation-based evaluation

Abstract During the past few years, the commercial Web search engines have augmented their underlying index structures by significantly enriching the information which describes the appearance of a word within a document Dean (2009) [7] . This enriched information is now used in complex and effective functions which rank documents by taking into consideration hundreds of features, with respect to a user query. Despite the evolution of the search engines, the past research has mainly concentrated on improving plain Web indexes storing typical data only. In this work we study the problem of organizing an inverted index storing additional information. In particular, we examine how the physical locations of a document, called zones, can be efficiently integrated with such an index structure. We introduce TZP, an encoder which compresses these zones in combination to the positions of a word in a document, by employing a fixed number of bits for each portion of a word’s inverted list. We demonstrate that our method allows direct access to the compressed zones and positions without expensive look-ups, avoids decoding any unnecessary information, while its overall index size is analogous or even better when compared against state-of-the art schemes. Moreover, we examine how the word positions can be combined to the zones to improve retrieval effectiveness. We introduce BM25TOPF, a scheme which incorporates term proximity and zone weighting into a single ranking formula. Unlike other term proximity approaches, BM25TOPF also takes into account the ordering of the query terms by rewarding the documents containing them in the correct order. Our experiments with the Web Adhoc Task of TREC 2009 and a set of own queries show that BM25TOPF outperforms the current state-of-the-art approaches by a margin between 6% and 11%.

A supervised machine learning classification algorithm for research articles

The issue of the automatic classification of research articles into one or more fields of science is of primary importance for scientific databases and digital libraries. A sophisticated classification strategy renders searching more effective and assists the users in locating similar relevant items. Although the most publishing services require from the authors to categorize their articles themselves, there are still cases where older documents remain unclassified, or the taxonomy changes over time. In this work we attempt to address this interesting problem by introducing a machine learning algorithm which combines several parameters and meta-data of a research article. In particular, our model exploits the training set to correlate keywords, authors, co-authorship, and publishing journals to a number of labels of the taxonomy. In the sequel, it applies this information to classify the rest of the documents. The experiments we have conducted with a large dataset comprised of about 1,5 million articles, demonstrate that in this specific application, our model outperforms the AdaBoost.MH and SVM methods.

Identifying attractive research fields for new scientists

Prior to the beginning of a scientific career, every new scientist is obliged to confront the critical issue of defining the subject area where his/her future research will be conducted. Regardless of the capabilities of a new scholar, an erroneous selection may condemn a dignified effort and result in wasted energy, time and resources. In this article we attempt to identify the research fields which are attractive to these individuals. To the best of our knowledge, this is a new topic that has never been discussed or addressed in the literature. Here we formally set the problem and we propose a solution combining the characteristics of the attractive research areas and the new scholars. Our approach is compared against a statistical model which reveals popular research areas. The comparison of this method to our proposed model leads to the conclusion that not all trendy research areas are suitable for new scientists. A secondary outcome reveals the existence of scientific fields which although they are not so emerging, they are promising for scientists who are starting their career.

Effective Ranking Fusion Methods for Personalized Metasearch Engines

Metasearch engines are a significant part of the information retrieval process. Most of Web users use them directly or indirectly to access information from more than one data sources. The cornerstone of their technology is their rank aggregation method, which is the algorithm they use to classify the collected results. In this paper we present three new rank aggregation methods. At first, we propose a method that takes into consideration the regional data for the user and the pages and assigns scores according to a variety of user defined parameters. In the second expansion, not all component engines are treated equally. The user is free to define the importance of each engine by setting appropriate weights. The third algorithm is designed to classify pages having URLs that contain subdomains. The three presented methods are combined into a single, personalized scoring formula, the global KE. All algorithms have been implemented in QuadSearch, an experimental metasearch engine available at http://quadsearch.csd.auth.gr.

Modern Web Technologies

Nowadays, World Wide Web is one of the most significant tools that people employ to seek information, locate new sources of knowledge, communicate, share ideas and experiences or even purchase products and make online bookings. The technologies adopted by the modern Web applications are being discussed in this book chapter. We summarize the most fundamental principles employed by the Web such as the client-server model and the http protocol and then we continue by presenting the current trends such as asynchronous communications, distributed applications, cloud computing and mobile Web applications. Finally, we conduct a short discussion regarding the future of the Web and the technologies that are going to play key roles in the deployment of novel applications.