Temporal Information Models for Real-Time Microblog Search

Real-time search in Twitter and other social media services is often biased towards the most recent results due to the “in the moment” nature of topic trends and their ephemeral relevance to users and media in general. However, “in the moment”, it is often difficult to look at all emerging topics and single-out the important ones from the rest of the social media chatter. This thesis proposes to leverage on external sources to estimate the duration and burstiness of live Twitter topics. It extends preliminary research where itwas shown that temporal re-ranking using external sources could indeed improve the accuracy of results. To further explore this topic we pursued three significant novel approaches: (1) multi-source information analysis that explores behavioral dynamics of users, such as Wikipedia live edits and page view streams, to detect topic trends and estimate the topic interest over time; (2) efficient methods for federated query expansion towards the improvement of query meaning; and (3) exploiting multiple sources towards the detection of temporal query intent. It differs from past approaches in the sense that it will work over real-time queries, leveraging on live user-generated content. This approach contrasts with previous methods that require an offline preprocessing step

Tracking Events in Social Media

Tracking topical events in social media streams, such as Twitter, provides a means for users to keep up-to-date on topics of interest to them. This tracking may last a period of days, or even weeks. These events and topics might be provided by users explicitly, or generated for users from selected news articles. Push notification from social media provides a method to push the updates directly to the users on their mobile devices or desktops. In this thesis, we start with a lexical comparison between carefully edited prose and social media posts, providing an improved understanding of word usage within social media. Compared with carefully edited prose, such as news articles and Wikipedia articles, the language of social media is informal in the extreme. By using word embeddings, we identify words whose usage differs greatly between a Wikipedia corpus and a Twitter corpus. Following from this work, we explore a general method for developing succinct queries, reflecting the topic of a given news article, for the purpose of tracking the associated news event within a social media stream. A series of probe queries are generated from an initial set of candidate keywords extracted from the article. By analyzing the results of these probes, we rank and trim the candidate set to create a succinct query. The method can also be used for linking and searching among different collections. Given a query for topical events, push notification to users directly from social media streams provides a method for them to keep up-to-date on topics of personal interest. We determine that the key to effective notification lies in controlling of update volume, by establishing and maintaining appropriate thresholds for pushing updates. We explore and evaluate multiple threshold setting strategies. Push notifications should be relevant to the personal interest, and timely, with pushes occurring as soon as after the actual event occurrence as possible and novel for providing non-duplicate information. An analysis of existing evaluation metrics for push notification reflects different assumptions regarding user requirements. This analysis leads to a framework that places different weights and penalties on different behaviours and can guide the future development of a family of evaluation metrics that more accurately models user needs. Throughout the thesis, rank similarity measures are applied to compare rankings generated by various experiments. As a final component, we develop a family of rank similarity metrics based on maximized effectiveness difference, each derived from a traditional information retrieval evaluation measure. Computing this maximized effectiveness difference (MED) requires the solution of an optimization problem that varies in difficulty, depending on the associated measure. We present solutions for several standard effectiveness measures, including nDCG, MAP, and ERR. Through experimental validation, we show that MED reveals meaningful differences between retrieval runs. Mathematically, MED is a metric, regardless of the associated measure. Prior work has established a number of other desiderata for rank similarity in the context of search, and we demonstrate that MED satisfies these requirements. Unlike previous proposals, MED allows us to directly translate assumptions about user behavior from any established effectiveness measure to create a corresponding rank similarity measure. In addition, MED cleanly accommodates partial relevance judgments, and if complete relevance information is available, it reduces to a simple difference between effectiveness values

GuruFinder

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2018, Tutor: José Mena i Jordi Vitrià i Marca[en] Imagine you are reading a newspaper, a blog, a scientific publication or a forum and you have become interested in a certain topic. After reading that site you may want to know more about it, so you click in a button and GuruFinder propose you a list of Twitter users to follow experts on that topic. The concept may be easy but the technology required underneath implies the combination of different disciplines including natural language processing, recommender systems, text mining, knowledge management systems and big data processing. GuruFinders pretends to explore state of the art techniques to build a prototype able to handle and process big volumes of tweets and offer real-time responses to the users

End-to-end Neural Information Retrieval

In recent years we have witnessed many successes of neural networks in the information retrieval community with lots of labeled data. Yet it remains unknown whether the same techniques can be easily adapted to search social media posts where the text is much shorter. In addition, we find that most neural information retrieval models are compared against weak baselines. In this thesis, we build an end-to-end neural information retrieval system using two toolkits: Anserini and MatchZoo. In addition, we also propose a novel neural model to capture the relevance of short and varied tweet text, named MP-HCNN. With the information retrieval toolkit Anserini, we build a reranking architecture based on various traditional information retrieval models (QL, QL+RM3, BM25, BM25+RM3), including a strong pseudo-relevance feedback baseline: RM3. With the neural network toolkit MatchZoo, we offer an empirical study of a number of popular neural network ranking models (DSSM, CDSSM, KNRM, DUET, DRMM). Experiments on datasets from the TREC Microblog Tracks and the TREC Robust Retrieval Track show that most existing neural network models cannot beat a simple language model baseline. How- ever, DRMM provides a significant improvement over the pseudo-relevance feedback baseline (BM25+RM3) on the Robust04 dataset and DUET, DRMM and MP-HCNN can provide significant improvements over the baseline (QL+RM3) on the microblog datasets. Further detailed analyses suggest that searching social media and searching news articles exhibit several different characteristics that require customized model design, shedding light on future directions

Entity-Oriented Search

This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms