15 research outputs found
Patent Citation Dynamics Modeling via Multi-Attention Recurrent Networks
Modeling and forecasting forward citations to a patent is a central task for
the discovery of emerging technologies and for measuring the pulse of inventive
progress. Conventional methods for forecasting these forward citations cast the
problem as analysis of temporal point processes which rely on the conditional
intensity of previously received citations. Recent approaches model the
conditional intensity as a chain of recurrent neural networks to capture memory
dependency in hopes of reducing the restrictions of the parametric form of the
intensity function. For the problem of patent citations, we observe that
forecasting a patent's chain of citations benefits from not only the patent's
history itself but also from the historical citations of assignees and
inventors associated with that patent. In this paper, we propose a
sequence-to-sequence model which employs an attention-of-attention mechanism to
capture the dependencies of these multiple time sequences. Furthermore, the
proposed model is able to forecast both the timestamp and the category of a
patent's next citation. Extensive experiments on a large patent citation
dataset collected from USPTO demonstrate that the proposed model outperforms
state-of-the-art models at forward citation forecasting
LaTeX: Language Pattern-aware Triggering Event Detection for Adverse Experience during Pandemics
The COVID-19 pandemic has accentuated socioeconomic disparities across
various racial and ethnic groups in the United States. While previous studies
have utilized traditional survey methods like the Household Pulse Survey (HPS)
to elucidate these disparities, this paper explores the role of social media
platforms in both highlighting and addressing these challenges. Drawing from
real-time data sourced from Twitter, we analyzed language patterns related to
four major types of adverse experiences: loss of employment income (LI), food
scarcity (FS), housing insecurity (HI), and unmet needs for mental health
services (UM). We first formulate a sparsity optimization problem that extracts
low-level language features from social media data sources. Second, we propose
novel constraints on feature similarity exploiting prior knowledge about the
similarity of the language patterns among the adverse experiences. The proposed
problem is challenging to solve due to the non-convexity objective and
non-smoothness penalties. We develop an algorithm based on the alternating
direction method of multipliers (ADMM) framework to solve the proposed
formulation. Extensive experiments and comparisons to other models on
real-world social media and the detection of adverse experiences justify the
efficacy of our model.Comment: arXiv admin note: text overlap with arXiv:1911.0868
Social media use among American Indians in South Dakota: Preferences and perceptions
Social media use data is widely being used in health, psychology, and
marketing research to analyze human behavior. However, we have very limited
knowledge on social media use among American Indians. In this context, this
study was designed to assess preferences and perceptions of social media use
among American Indians during COVID-19. We collected data from American Indians
in South Dakota using online survey. Results show that Facebook, YouTube,
TikTok, Instagram and Snapchat are the most preferred social media platforms.
Most of the participants reported that the use of social media increased
tremendously during COVID-19 and had perceptions of more negative effects than
positive effects. Hate/harassment/extremism, misinformation/made up news, and
people getting one point of view were the top reasons for negative effects.Comment: 20 pages, 6 figures, 2 Tables, Appendix Tables (7
Stock Movement and Volatility Prediction from Tweets, Macroeconomic Factors and Historical Prices
Predicting stock market is vital for investors and policymakers, acting as a
barometer of the economic health. We leverage social media data, a potent
source of public sentiment, in tandem with macroeconomic indicators as
government-compiled statistics, to refine stock market predictions. However,
prior research using tweet data for stock market prediction faces three
challenges. First, the quality of tweets varies widely. While many are filled
with noise and irrelevant details, only a few genuinely mirror the actual
market scenario. Second, solely focusing on the historical data of a particular
stock without considering its sector can lead to oversight. Stocks within the
same industry often exhibit correlated price behaviors. Lastly, simply
forecasting the direction of price movement without assessing its magnitude is
of limited value, as the extent of the rise or fall truly determines
profitability. In this paper, diverging from the conventional methods, we
pioneer an ECON. The framework has following advantages: First, ECON has an
adept tweets filter that efficiently extracts and decodes the vast array of
tweet data. Second, ECON discerns multi-level relationships among stocks,
sectors, and macroeconomic factors through a self-aware mechanism in semantic
space. Third, ECON offers enhanced accuracy in predicting substantial stock
price fluctuations by capitalizing on stock price movement. We showcase the
state-of-the-art performance of our proposed model using a dataset,
specifically curated by us, for predicting stock market movements and
volatility
ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction
For both investors and policymakers, forecasting the stock market is
essential as it serves as an indicator of economic well-being. To this end, we
harness the power of social media data, a rich source of public sentiment, to
enhance the accuracy of stock market predictions. Diverging from conventional
methods, we pioneer an approach that integrates sentiment analysis,
macroeconomic indicators, search engine data, and historical prices within a
multi-attention deep learning model, masterfully decoding the complex patterns
inherent in the data. We showcase the state-of-the-art performance of our
proposed model using a dataset, specifically curated by us, for predicting
stock market movements and volatility
PanTop: Pandemic Topic Detection and Monitoring System (Student Abstract)
Diverse efforts to combat the COVID-19 pandemic have continued throughout the past two years. Governments have announced plans for unprecedentedly rapid vaccine development, quarantine measures, and economic revitalization. They contribute to a more effective pandemic response by determining the precise opinions of individuals regarding these mitigation measures. In this paper, we propose a deep learning-based topic monitoring and storyline extraction system for COVID-19 that is capable of analyzing public sentiment and pandemic trends. The proposed method is able to retrieve Twitter data related to COVID-19 and conduct spatiotemporal analysis. Furthermore, a deep learning component of the system provides monitoring and modeling capabilities for topics based on advanced natural language processing models. A variety of visualization methods are applied to the project to show the distribution of each topic. Our proposed system accurately reflects how public reactions change over time along with pandemic topics
Augmentation of Chinese Character Representations with Compositional Graph Learning (Student Abstract)
Chinese characters have semantic-rich compositional information in radical form. While almost all previous research has applied CNNs to extract this compositional information, our work utilizes deep graph learning on a compact, graph-based representation of Chinese characters. This allows us to exploit temporal information within the strict stroke order used in writing characters. Our results show that our stroke-based model has potential for helping large-scale language models on some Chinese natural language understanding tasks. In particular, we demonstrate that our graph model produces more interpretable embeddings shown through word subtraction analogies and character embedding visualizations
Early Forecast of Traffic Accident Impact Based on a Single-Snapshot Observation (Student Abstract)
Predicting and quantifying the impact of traffic accidents is necessary and critical to Intelligent Transport Systems (ITS). As a state-of-the-art technique in graph learning, current graph neural networks heavily rely on graph Fourier transform, assuming homophily among the neighborhood. However, the homophily assumption makes it challenging to characterize abrupt signals such as traffic accidents. Our paper proposes an abrupt graph wavelet network (AGWN) to model traffic accidents and predict their time durations using only one single snapshot