Given the extremely large pool of events and stories available, media outlets need to focus on a subset of issues and aspects to convey to their audience. Outlets are often accused of exhibiting a systematic bias in this selection process, with different outlets portraying different versions of reality. However, in the absence of objective measures and empirical evidence, the direction and extent of systematicity remains widely disputed. In this paper we propose a framework based on quoting patterns for quantifying and characterizing the degree to which media outlets exhibit systematic bias. We apply this framework to a massive dataset of news articles spanning the six years of Obama's presidency and all of his speeches, and reveal that a systematic pattern does indeed emerge from the outlet's quoting behavior. Moreover, we show that this pattern can be successfully exploited in an unsupervised prediction setting, to determine which new quotes an outlet will select to broadcast. By encoding bias patterns in a low-rank space we provide an analysis of the structure of political media coverage. This reveals a latent media bias space that aligns surprisingly well with political ideology and outlet type. A linguistic analysis exposes striking differences across these latent dimensions, showing how the different types of media outlets portray different realities even when reporting on the same events. For example, outlets mapped to the mainstream conservative side of the latent space focus on quotes that portray a presidential persona disproportionately characterized by negativity.
Quote tracking Recent work has focused quoting practices @cite_23 and on the task of efficiently tracking and matching quote snippets as they evolve, both over a set period of time @cite_19 , as well as over an longer, variable period of time @cite_31 . We adapt this task in order to news article quotes with presidential speech segments and build our outlet-to-quote graph.
@cite_19 @cite_31 @cite_23
2127492100 2137449855
Given the extremely large pool of events and stories available, media outlets need to focus on a subset of issues and aspects to convey to their audience. Outlets are often accused of exhibiting a systematic bias in this selection process, with different outlets portraying different versions of reality. However, in the absence of objective measures and empirical evidence, the direction and extent of systematicity remains widely disputed. In this paper we propose a framework based on quoting patterns for quantifying and characterizing the degree to which media outlets exhibit systematic bias. We apply this framework to a massive dataset of news articles spanning the six years of Obama's presidency and all of his speeches, and reveal that a systematic pattern does indeed emerge from the outlet's quoting behavior. Moreover, we show that this pattern can be successfully exploited in an unsupervised prediction setting, to determine which new quotes an outlet will select to broadcast. By encoding bias patterns in a low-rank space we provide an analysis of the structure of political media coverage. This reveals a latent media bias space that aligns surprisingly well with political ideology and outlet type. A linguistic analysis exposes striking differences across these latent dimensions, showing how the different types of media outlets portray different realities even when reporting on the same events. For example, outlets mapped to the mainstream conservative side of the latent space focus on quotes that portray a presidential persona disproportionately characterized by negativity. Political speeches and debates play an important role in shaping the images of politicians, and the public often relies on media outlets to select bits of political communication from a large pool of utterances. It is an important research question to understand what factors impact this selection process. To quantitatively explore the selection process, we build a three-decade dataset of presidential debate transcripts and post-debate coverage. We first examine the effect of wording and propose a binary classification framework that controls for both the speaker and the debate situations. We find that crowdworkers can only achieve an accuracy of 60 in this task, indicating that media choices are not entirely obvious. Our classifiers outperform crowdworkers on average, mainly in primary debates. We also compare important factors from crowdworkers’ free responses with those from data-driven methods and find interesting differences. Few crowdworkers mentioned that “context matters”, whereas our data show that well-quoted sentences are more distinct from the previous utterance by the same speaker than less-quoted sentences. Finally, we examine the aggregate effect of media preferences towards different wordings to understand the extent of fragmentation among media outlets. By analyzing a bipartite graph built from quoting behavior in our data, we observe a decreasing trend in bipartisan coverage. Understanding the ways in which information achieves widespread public awareness is a research question of significant interest. We consider whether, and how, the way in which the information is phrased --- the choice of words and sentence structure --- can affect this process. To this end, we develop an analysis framework and build a corpus of movie quotes, annotated with memorability information, in which we are able to control for both the speaker and the setting of the quotes. We find that there are significant differences between memorable and non-memorable quotes in several key dimensions, even after controlling for situational and contextual factors. One is lexical distinctiveness: in aggregate, memorable quotes use less common word choices, but at the same time are built upon a scaffolding of common syntactic patterns. Another is that memorable quotes tend to be more general in ways that make them easy to apply in new contexts --- that is, more portable. We also show how the concept of "memorable language" can be extended across domains. How do journalists mark quoted content as certain or uncertain, and how do readers interpret these signals? Predicates such as thinks, claims, and admits offer a range of options for framing quoted content according to the author’s own perceptions of its credibility. We gather a new dataset of direct and indirect quotes from Twitter, and obtain annotations of the perceived certainty of the quoted statements. We then compare the ability of linguistic and extra-linguistic features to predict readers’ assessment of the certainty of quoted content. We see that readers are indeed influenced by such framing devices — and we find no evidence that they consider other factors, such as the source, journalist, or the content itself. In addition, we examine the impact of specific framing devices on perceptions of credibility. We analyse the usage of quotes in forum.rpg.net, the largest online forum on tabletop roleplaying games. Quote usage appears pervasive and surprisingly consistent over time and users; it seems to have a role in aiding intra-thread navigation; and it reveals an underlying "social" structure in a community that otherwise lacks all trappings (from friends and followers to reputations) of today's social networks. This is the first work to investigate community structure and interaction through the lens of quotes in an online forum. In this paper we investigate the effect of the context of interaction on the extent to which a contributor's perspective bias is displayed through their lexical choice. We present a series of experiments on political discussion data. Our experiments indicate that (i) when people quote contributors with an opposing view, they tend to quote the words that are less strongly associated with the opposing view. (ii) Nevertheless, in quoting their opponents, the displayed bias of their word distributions shifts towards that of their opponents. (iii) The personal bias of the speaker is displayed most clearly through the words that are not quoted, (iv) although characteristics of the quoted message do have a measurable effect on the words that are included in the contribution. And, finally, (v) posts are influenced by the displayed bias of previous posts in a thread. The authors describe efficient methods for screening clone libraries, based on pooling schemes called open_quotes random k-sets designs. close_quotes In these designs, the pools in which any clone occurs are equally likely to be any possible selection of k from the v pools. The values of k and v can be chosen to optimize desirable properties. Random k-sets designs have substantial advantages over alternative pooling schemes: they are efficient, flexible, and easy to specify, require fewer pools, and have error-correcting and error-detecting capabilities. In addition, screening can often be achieved in only one pass, thus facilitating automation. For design comparison, the authors assume a binomial distribution for the number of open_quotes positive close_quotes clones, with parameters n, the number of clones, and c, the coverage. They propose the expected number of resolved positive clones-clones that are definitely positive based upon the pool assays-as a criterion for the efficiency of a pooling design. They determine the value of k that is optimal, with respect to this criterion, as a function of v, n, and c. They also describe superior k-sets designs called k-sets packing designs. As an illustration, they discuss a robotically implemented design for a 2.5-fold-coverage, human chromosome 16 YAC library of n =more » 1298 clones. They also estimate the probability that each clone is positive, given the pool-assay data and a model for experimental errors. 23 refs., 4 figs« less It is crucial for a web crawler to distinguish between ephemeral and persistent content. Ephemeral content (e.g., quote of the day) is usually not worth crawling, because by the time it reaches the index it is no longer representative of the web page from which it was acquired. On the other hand, content that persists across multiple page updates (e.g., recent blog postings) may be worth acquiring, because it matches the page's true content for a sustained period of time. In this paper we characterize the longevity of information found on the web, via both empirical measurements and a generative model that coincides with these measurements. We then develop new recrawl scheduling policies that take longevity into account. As we show via experiments over real web data, our policies obtain better freshness at lower cost, compared with previous approaches. Thermodynamic depth is an appealing but flawed structural complexity measure. It depends on a set of macroscopic states for a system, but neither its original introduction by Lloyd and Pagels nor any follow-up work has considered how to select these states. Depth, therefore, is at root arbitrary. Computational mechanics, an alternative approach to structural complexity, provides a definition for a system close_quote s minimal, necessary causal states and a procedure for finding them. We show that the rate of increase in thermodynamic depth, or ital dive , is the system close_quote s reverse-time Shannon entropy rate, and so depth only measures degrees of macroscopic randomness, not structure. To fix this, we redefine the depth in terms of the causal state representation emdash epsilon -machines emdash and show that this representation gives the minimum dive consistent with accurate prediction. Thus, epsilon -machines are optimally shallow. copyright ital 1999 ital The American Physical Society The range and sparsity of the one-electron density matrix (DM) in density functional theory is studied for large systems using the analytical properties of its Chebyshev expansion. General estimates of the range of the DM are derived, showing that the range is inversely proportional to the square root of an insulator band gap and inversely proportional to the square root of the temperature. These findings support open_quotes principle of nearsightedness close_quotes introduced recently by W. Kohn [Phys.Rev.Lett. bold 76 , 3168 (1996)]. These estimates are used to study the complexity of several linear system-size scaling electronic structure algorithms which differ in their dependence on the geometric dimensionality of the system. copyright ital 1997 ital The American Physical Society We compute the single-particle density matrix in large (500-, 512-, and 1000-atom) models of fcc aluminum and crystalline (diamond) and amorphous silicon and carbon. We use an approximate density functional Hamiltonian in the local density approximation. The density matrix for fcc aluminum is found to closely approximate the results for jellium, and the crystalline and amorphous insulators exhibit exponential decay albeit with pronounced anisotropy. We compare the computed decays to existing predictions of the fall off of the density matrix in insulators and find that the open_quotes tight-binding close_quotes prediction of Kohn [W. Kohn, Phys. Rev. >115, 809 (1959)] provides the best overall fit to our calculations for Si and C. In this paper we devise new paging heuristics motivated by the access graph model of paging. Unlike the access graph model and the related Markov paging model our heuristics are truly on-line in that we do not assume any prior knowledge of the program just about to be executed. The Least Recently Used heuristic for paging is remarkably good, and is known experimentally to be superior to many of the suggested alternatives on real program traces. Experiments we ve performed suggest that our heuristics beat LRU fairly consistently, over a wide range of cache sizes and programs. The number of page faults can be as low as 1 2 the number of page faults for LRU and is on average 7 to 9 percent less than the number of faults for LRU. (Depending on how this average is computed.) We have built a program tracer that gives the page access sequence for real program executions of 200 - 3,300 thousand page access requests, and our simulations are based on 25 of these program traces. Overall, we have performed several thousand such simulations. While we have no real evidence to suggest that the programs we ve traced are typical in any sense, we havemore » made use of an experimental open_quotes protocol close_quotes designed to avoid experimenter bias. We strongly emphasize that our results are only preliminary and that much further work needs to be done.« less Our research examines a predictive machine learning approach for financial news articles analysis using several different textual representations: bag of words, noun phrases, and named entities. Through this approach, we investigated 9,211 financial news articles and 10,259,042 stock quotes covering the SP directional accuracy) and the highest return using a simulated trading engine (2.06p return). We further investigated the different textual representations and found that a Proper Noun scheme performs better than the de facto standard of Bag of Words in all three metrics. The availability of high-frequency data on transactions, quotes, and order flow in electronic order-driven markets has revolutionized data processing and statistical modeling techniques in finance and brought up new theoretical and computational challenges. Market dynamics at the transaction level cannot be characterized solely in terms the dynamics of a single price, and one must also take into account the interaction between buy and sell orders of different types by modeling the order flow at the bid price, ask price, and possibly other levels of the limit order book. Incivility in public discussions has received increasing attention from academic and popular commentators in recent years. In an effort to better understand the nature and determinants of such incivility, this study examined a 3-week census of articles and comments posted to a local newspaper’s website—totaling more than 300 articles and 6,400 comments. The results of the content analysis show that incivility occurs frequently and is associated with key contextual factors, such as the topic of the article and the sources quoted within the article. We also find that, contrary to popular perceptions, frequent commenters are more civil than are infrequent commenters, and uncivil commenters are no less likely than civil commenters to use evidence in support of their claims. The development of online news media grew in number in Indonesia. One technique of news articles summarization is guided summarization where the summary should contain important aspect information. Guided summarization techniques have been developed in the Text Analysis Conference (TAC) 2011 and one of the best methods is SWING by Jun-ping, et al The purpose of this study is to adapt the methods of SWING system to Indonesian news articles as well as integrating with News Aggregator system. In this research, the experiments have purpose to determine the best features and system configuration when adapted to Indonesian news articles. ROUGE-2 and ROUGE-SU4 is used to evaluate the results of the summary where a summary of the system results compared to the human-made summaries. The best system configuration produces summary with evaluation of ROUGE-2 0,31 and ROUGE-SU4 0,22 which is very close to the human-made summaries with a value of ROUGE-2 0,32 and ROUGE-SU4 0.24. In addition, the update summarization component can be run by giving a summary of updates without repeating the information. Adaptation from SWING system to Indonesian news articles is employing features such as sentence length (SL), category relevance score (CRS), category KL-Divergence (CKLD), bigram DFS (BDFS), Top n NE corpus, Top n NE topic, quote sentence removal, and building SVR model for each news category. A low-energy background field solution is presented which describes several D-membranes oriented at angles with respect to one another. The mass and charge densities for this configuration are computed and found to saturate the Bogomol close_quote nyi-Prasad-Sommerfeld bound, implying the preservation of one-quarter of the supersymmetries. T duality is exploited to construct new solutions with nontrivial angles from the basic one. copyright ital 1997 ital The American Physical Society Social media is playing an increasingly vital role in information dissemination. But with dissemination being more distributed, content often makes multiple hops, and consequently has opportunity to change. In this paper we focus on content that should be changing the least, namely quoted text. We find changes to be frequent, with their likelihood depending on the authority of the copied source and the type of site that is copying. We uncover patterns in the rate of appearance of new variants, their length, and popularity, and develop a simple model that is able to capture them. These patterns are distinct from ones produced when all copies are made from the same source, suggesting that information is evolving as it is being processed collectively in online social media. Many applications collect a large number of time series, for example, the financial data of companies quoted in a stock exchange, the health care data of all patients that visit the emergency room of a hospital, or the temperature sequences continuously measured by weather stations across the US. These data are often referred to as un structured. The first task in its analytics is to derive a low dimensional representation, a graph or discrete manifold, that describes well the inter relations among the time series and their intra relations across time. This paper presents a computationally tractable algorithm for estimating this graph that structures the data. The resulting graph is directed and weighted, possibly capturing causal relations, not just reciprocal correlations as in many existing approaches in the literature. A convergence analysis is carried out. The algorithm is demonstrated on random graph datasets and real network time series datasets, and its performance is compared to that of related methods. The adjacency matrices estimated with the new method are close to the true graph in the simulated data and consistent with prior physical knowledge in the real dataset tested. Recurrent Neural Networks (RNNs), and specifically a variant with Long Short-Term Memory (LSTM), are enjoying renewed interest as a result of successful applications in a wide range of machine learning problems that involve sequential data. However, while LSTMs provide exceptional results in practice, the source of their performance and their limitations remain rather poorly understood. Using character-level language models as an interpretable testbed, we aim to bridge this gap by providing an analysis of their representations, predictions and error types. In particular, our experiments reveal the existence of interpretable cells that keep track of long-range dependencies such as line lengths, quotes and brackets. Moreover, our comparative analysis with finite horizon n-gram models traces the source of the LSTM improvements to long-range structural dependencies. Finally, we provide analysis of the remaining errors and suggests areas for further study.