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Eliana Providel

Bio: Eliana Providel is an academic researcher from University of Chile. The author has contributed to research in topics: Computer science & Rank (computer programming). The author has an hindex of 1, co-authored 1 publications receiving 90 citations.

Papers
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Book ChapterDOI
07 Jun 2012
TL;DR: This paper presents two structures, one using the bitmap in plain form and another using a compressed form, that are simple to implement and combine much lower space overheads than previous work with excellent time performance for rank and select queries.
Abstract: Rank and select queries on bitmaps are fundamental for the construction of a variety of compact data structures. Both can, in theory, be answered in constant time by spending o(n) extra bits on top of the original bitmap, of length n, or of a compressed version of it. However, while the solution for rank is indeed simple and practical, a similar result for select has been elusive, and practical compact data structure implementations avoid its use whenever possible. In addition, the overhead of the o(n) extra bits is in many cases very significant. In this paper we bridge the gap between theory and practice by presenting two structures, one using the bitmap in plain form and another using a compressed form, that are simple to implement and combine much lower space overheads than previous work with excellent time performance for rank and select queries. In particular, our structure for plain bitmaps is far smaller and faster for select than any previous structure, while competitive for rank with the best previous structures of similar size.

96 citations

Journal ArticleDOI
TL;DR: In this article , the authors analyzed data from Twitter, Facebook, and Instagram to understand the spread of false and inaccurate information online, and they found that false information spreads faster and reaches more users than true information on Twitter and Facebook.
Abstract: Information disorders on social media can have a significant impact on citizens’ participation in democratic processes. To better understand the spread of false and inaccurate information online, this research analyzed data from Twitter, Facebook, and Instagram. The data were collected and verified by professional fact-checkers in Chile between October 2019 and October 2021, a period marked by political and health crises. The study found that false information spreads faster and reaches more users than true information on Twitter and Facebook. Instagram, on the other hand, seemed to be less affected by this phenomenon. False information was also more likely to be shared by users with lower reading comprehension skills. True information, on the other hand, tended to be less verbose and generate less interest among audiences. This research provides valuable insights into the characteristics of misinformation and how it spreads online. By recognizing the patterns of how false information diffuses and how users interact with it, we can identify the circumstances in which false and inaccurate messages are prone to becoming widespread. This knowledge can help us to develop strategies to counter the spread of misinformation and protect the integrity of democratic processes.
Proceedings ArticleDOI
17 Oct 2022
TL;DR: A new dataset with Twitter claims verified by fact-checkers along with the propagation structure of retweets and replies is presented, which helps understand the phenomenon of disinformation during social crises being one of the first of its kind to be released.
Abstract: Disinformation is one of the main threats that loom on social networks. Detecting disinformation is not trivial and requires training and maintaining fact-checking teams, which is labor-intensive. Recent studies show that the propagation structure of claims and user messages allows a better understanding of rumor dynamics. Despite these findings, the availability of verified claims and structural propagation data is low. This paper presents a new dataset with Twitter claims verified by fact-checkers along with the propagation structure of retweets and replies. The dataset contains verified claims checked during the Chilean social outbreak, which allows for studying the phenomenon of disinformation during this crisis. We study propagation patterns of verified content in CLNews, showing differences between false rumors and other types of content. Our results show that false rumors are more persistent than the rest of verified contents, reaching more people than truthful news and presenting low barriers of readability to users. The dataset is fully available and helps understand the phenomenon of disinformation during social crises being one of the first of its kind to be released.

Cited by
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Book ChapterDOI
29 Jun 2014
TL;DR: This paper presents a framework for experimentation with succinct data structures, providing a large set of configurable components, together with tests, benchmarks, and tools to analyze resource requirements.
Abstract: Engineering efficient implementations of compact and succinct structures is time-consuming and challenging, since there is no standard library of easy-to-use, highly optimized, and composable components. One consequence is that measuring the practical impact of new theoretical proposals is difficult, since older baseline implementations may not rely on the same basic components, and reimplementing from scratch can be time-consuming. In this paper we present a framework for experimentation with succinct data structures, providing a large set of configurable components, together with tests, benchmarks, and tools to analyze resource requirements. We demonstrate the functionality of the framework by recomposing two succinct solutions for top-k document retrieval which can operate on both character and integer alphabets.

312 citations

Posted Content
TL;DR: In this article, the authors present a framework for experimentation with succinct data structures, providing a large set of configurable components, together with tests, benchmarks, and tools to analyze resource requirements.
Abstract: Engineering efficient implementations of compact and succinct structures is a time-consuming and challenging task, since there is no standard library of easy-to- use, highly optimized, and composable components. One consequence is that measuring the practical impact of new theoretical proposals is a difficult task, since older base- line implementations may not rely on the same basic components, and reimplementing from scratch can be very time-consuming. In this paper we present a framework for experimentation with succinct data structures, providing a large set of configurable components, together with tests, benchmarks, and tools to analyze resource requirements. We demonstrate the functionality of the framework by recomposing succinct solutions for document retrieval.

291 citations

Journal ArticleDOI
TL;DR: This survey gives an overview of wavelet trees and the surprising number of applications in which they are useful: basic and weighted point grids, sets of rectangles, strings, permutations, binary relations, graphs, inverted indexes, document retrieval indexes, full-text indexes, XML indexes, and general numeric sequences.

151 citations

Proceedings ArticleDOI
27 May 2018
TL;DR: It is shown that replacing RocksDB's Bloom filters with SuRFs speeds up open-seek and closed-seek queries by up to 1.5× and 5× with a modest cost on the worst-case point query throughput due to slightly higher false positive rate.
Abstract: We present the Succinct Range Filter (SuRF), a fast and compact data structure for approximate membership tests. Unlike traditional Bloom filters, SuRF supports both single-key lookups and common range queries: open-range queries, closed-range queries, and range counts. SuRF is based on a new data structure called the Fast Succinct Trie (FST) that matches the point and range query performance of state-of-the-art order-preserving indexes, while consuming only 10 bits per trie node. The false positive rates in SuRF for both point and range queries are tunable to satisfy different application needs. We evaluate SuRF in RocksDB as a replacement for its Bloom filters to reduce I/O by filtering requests before they access on-disk data structures. Our experiments on a 100 GB dataset show that replacing RocksDB's Bloom filters with SuRFs speeds up open-seek (without upper-bound) and closed-seek (with upper-bound) queries by up to 1.5× and 5× with a modest cost on the worst-case (all-missing) point query throughput due to slightly higher false positive rate.

118 citations

Book ChapterDOI
03 Jul 2012
TL;DR: This survey gives an overview of wavelet trees and the surprising number of applications in which they are useful: basic and weighted point grids, sets of rectangles, strings, permutations, binary relations, graphs, inverted indexes, document retrieval indexes, full-text indexes, XML indexes, and general numeric sequences.
Abstract: The wavelet tree is a versatile data structure that serves a number of purposes, from string processing to geometry. It can be regarded as a device that represents a sequence, a reordering, or a grid of points. In addition, its space adapts to various entropy measures of the data it encodes, enabling compressed representations. New competitive solutions to a number of problems, based on wavelet trees, are appearing every year. In this survey we give an overview of wavelet trees and the surprising number of applications in which we have found them useful: basic and weighted point grids, sets of rectangles, strings, permutations, binary relations, graphs, inverted indexes, document retrieval indexes, full-text indexes, XML indexes, and general numeric sequences.

116 citations