An official website of the United States government
Many online news outlets, forums, and blogs provide a rich stream of publications and user comments. This rich body of data is a valuable source of information for researchers, journalists, and policymakers. However, the ever-increasing production and user engagement rate make it difficult to analyze this data without automated tools. This work presents MultiLayerET, a method to unify the representation of entities and topics in articles and comments. In MultiLayerET, articles' content and associated comments are parsed into a multilayer graph consisting of heterogeneous nodes representing named entities and news topics. The nodes within this graph have attributed edges denoting weight, i.e., the strength of the connection between the two nodes, time, i.e., the co-occurrence contemporaneity of two nodes, and sentiment, i.e., the opinion (in aggregate) of an entity toward a topic. Such information helps in analyzing articles and their comments. We infer the edges connecting two nodes using information mined from the textual data. The multilayer representation gives an advantage over a single-layer representation since it integrates articles and comments via shared topics and entities, providing richer signal points about emerging events. MultiLayerET can be applied to different downstream tasks, such as detecting media bias and misinformation. To explore the efficacy of the proposed method, we apply MultiLayerET to a body of data gathered from six representative online news outlets. We show that with MultiLayerET, the classification F1 score of a media bias prediction model improves by 36%, and that of a state-of-the-art fake news detection model improves by 4%.
more »
« less
Predicting Returns with Text Data
We introduce a new text-mining methodology that extracts information from news articles to predict asset returns. Unlike more common sentiment scores used for stock return prediction (e.g., those sold by commercial vendors or built with dictionary-based methods), our supervised learning framework constructs a score that is specifically adapted to the problem of return prediction. Our method proceeds in three steps: 1) isolating a list of terms via predictive screening, 2) assigning prediction weights to these words via topic modeling, and 3) aggregating terms into an article-level predictive score via penalized likelihood. We derive theoretical guarantees on the accuracy of estimates from our model with minimal assumptions. In our empirical analysis, we study one of the most actively monitored streams of news articles in the financial system--the Dow Jones Newswires--and show that our supervised text model excels at extracting return-predictive signals in this context. Information in newswires is assimilated into prices with an ineffcient delay that is broadly consistent with limits-to-arbitrage (i.e., more severe for smaller and more volatile firms) yet can be exploited in a real-time trading strategy with reasonable turnover and net of transaction costs.
more »
« less
- Award ID(s):
- 1925845
- PAR ID:
- 10289824
- Date Published:
- Journal Name:
- SSRN Electronic Journal
- ISSN:
- 1556-5068
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)Identifying the discourse structure of documents is an important task in understanding written text. Building on prior work, we demonstrate an improved approach to automatically identifying the discourse function of paragraphs in news articles. We start with the hierarchical theory of news discourse developed by van Dijk (1988) which proposes how paragraphs function within news articles. This discourse information is a level intermediate between phrase- or sentence-sized discourse segments and document genre, characterizing how individual paragraphs convey information about the events in the storyline of the article. Specifically, the theory categorizes the relationships between narrated events and (1) the overall storyline (such as Main Events, Background, or Consequences) as well as (2) commentary (such as Verbal Reactions and Evaluations). We trained and tested a linear chain conditional random field (CRF) with new features to model van Dijk’s labels and compared it against several machine learning models presented in previous work. Our model significantly outperformed all baselines and prior approaches, achieving an average of 0.71 F1 score which represents a 31.5% improvement over the previously best-performing support vector machine model.more » « less
-
Prior work on ideology prediction has largely focused on single modalities, i.e., text or images. In this work, we introduce the task of multimodal ideology prediction, where a model predicts binary or five-point scale ideological leanings, given a text-image pair with political content. We first collect five new large-scale datasets with English documents and images along with their ideological leanings, covering news articles from a wide range of mainstream media in US and social media posts from Reddit and Twitter. We conduct in-depth analyses on news articles and reveal differences in image content and usage across the political spectrum. Furthermore, we perform extensive experiments and ablation studies, demonstrating the effectiveness of targeted pretraining objectives on different model components. Our best performing model, a late-fusion architecture pretrained with a triplet objective over multimodal content, outperforms the state-of-the-art text-only model by almost 4% and a strong multimodal baseline with no pretraining by over 3%.more » « less
-
Automated journalism technology is transforming news production and changing how audiences perceive the news. As automated text-generation models advance, it is important to understand how readers perceive human-written and machine-generated content. This study used OpenAI’s GPT-2 text-generation model (May 2019 release) and articles from news organizations across the political spectrum to study participants’ reactions to human- and machine-generated articles. As participants read the articles, we collected their facial expression and galvanic skin response (GSR) data together with self-reported perceptions of article source and content credibility. We also asked participants to identify their political affinity and assess the articles’ political tone to gain insight into the relationship between political leaning and article perception. Our results indicate that the May 2019 release of OpenAI’s GPT-2 model generated articles that were misidentified as written by a human close to half the time, while human-written articles were identified correctly as written by a human about 70 percent of the time.more » « less
-
null (Ed.)Today social media has become the primary source for news. Via social media platforms, fake news travel at unprecedented speeds, reach global audiences and put users and communities at great risk. Therefore, it is extremely important to detect fake news as early as possible. Recently, deep learning based approaches have shown improved performance in fake news detection. However, the training of such models requires a large amount of labeled data, but manual annotation is time-consuming and expensive. Moreover, due to the dynamic nature of news, annotated samples may become outdated quickly and cannot represent the news articles on newly emerged events. Therefore, how to obtain fresh and high-quality labeled samples is the major challenge in employing deep learning models for fake news detection. In order to tackle this challenge, we propose a reinforced weakly-supervised fake news detection framework, i.e., WeFEND, which can leverage users' reports as weak supervision to enlarge the amount of training data for fake news detection. The proposed framework consists of three main components: the annotator, the reinforced selector and the fake news detector. The annotator can automatically assign weak labels for unlabeled news based on users' reports. The reinforced selector using reinforcement learning techniques chooses high-quality samples from the weakly labeled data and filters out those low-quality ones that may degrade the detector's prediction performance. The fake news detector aims to identify fake news based on the news content. We tested the proposed framework on a large collection of news articles published via WeChat official accounts and associated user reports. Extensive experiments on this dataset show that the proposed WeFEND model achieves the best performance compared with the state-of-the-art methods.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2139/ssrn.3389884
