Open Access. Powered by Scholars. Published by Universities.®
- Institution
-
- Singapore Management University (1112)
- TÜBİTAK (199)
- Wright State University (191)
- Technological University Dublin (182)
- Old Dominion University (126)
-
- Missouri University of Science and Technology (112)
- San Jose State University (111)
- City University of New York (CUNY) (93)
- Neutrosophic Systems with Applications (92)
- MBZUAI (70)
- Dartmouth College (67)
- Brigham Young University (65)
- University of Texas at El Paso (63)
- Chulalongkorn University (62)
- Zayed University (58)
- University of Nebraska - Lincoln (56)
- Edith Cowan University (50)
- Kennesaw State University (49)
- Boise State University (48)
- New Jersey Institute of Technology (44)
- California Polytechnic State University, San Luis Obispo (43)
- Chapman University (42)
- Embry-Riddle Aeronautical University (42)
- MMU Press (41)
- Purdue University (41)
- University of Nebraska at Omaha (40)
- University of Arkansas, Fayetteville (39)
- Montclair State University (37)
- University of Central Florida (36)
- University of South Florida (36)
- Keyword
-
- Machine learning (185)
- Natural language processing (184)
- Artificial intelligence (141)
- Natural Language Processing (134)
- Machine Learning (127)
-
- Deep learning (119)
- Artificial Intelligence (95)
- Large language models (84)
- Computer Science (80)
- Social media (80)
- Twitter (64)
- NLP (62)
- Deep Learning (61)
- Sentiment analysis (59)
- Large Language Models (56)
- Computational linguistics (53)
- Department of Computer Science and Engineering (53)
- Text mining (51)
- Neural networks (48)
- Generative AI (46)
- Data mining (44)
- AI (43)
- Computer science (40)
- Classification (37)
- Information retrieval (36)
- Social Media (36)
- Fuzzy logic (35)
- Semantics (34)
- Sentiment Analysis (34)
- LLM (32)
- Publication Year
- Publication
-
- Research Collection School Of Computing and Information Systems (1036)
- Turkish Journal of Electrical Engineering and Computer Sciences (199)
- Theses and Dissertations (162)
- Master's Projects (99)
- Conference papers (93)
-
- Neutrosophic Systems with Applications (92)
- Kno.e.sis Publications (84)
- Dissertations (70)
- Computer Science Faculty Research & Creative Works (66)
- Chulalongkorn University Theses and Dissertations (Chula ETD) (62)
- Browse all Theses and Dissertations (61)
- All Works (58)
- Computer Science Faculty Publications (58)
- Faculty Publications (49)
- Computer Science Faculty Publications and Presentations (45)
- Publications and Research (44)
- Journal of Informatics and Web Engineering (41)
- Articles (40)
- Electronic Theses and Dissertations (39)
- Computer Science and Engineering Faculty Publications (38)
- Natural Language Processing Faculty Publications (38)
- Departmental Technical Reports (CS) (36)
- Dissertations and Theses Collection (Open Access) (35)
- USF Tampa Graduate Theses and Dissertations (34)
- Computer Science: Faculty Publications (33)
- Dissertations, Theses, and Capstone Projects (32)
- Theses (31)
- CCAC Theses and Dissertations (30)
- Faculty Scholarship (28)
- Masters Theses (26)
- Publication Type
- File Type
Articles 2731 - 2760 of 4505
Full-Text Articles in Entire DC Network
On The Exactitude Of Big Data: La Bêtise And Artificial Intelligence, Noel Fitzpatrick, John D. Kelleher
On The Exactitude Of Big Data: La Bêtise And Artificial Intelligence, Noel Fitzpatrick, John D. Kelleher
Articles
This article revisits the question of ‘la bêtise’ or stupidity in the era of Artificial Intelligence driven by Big Data, it extends on the questions posed by Gille Deleuze and more recently by Bernard Stiegler. However, the framework for revisiting the question of la bêtise will be through the lens of contemporary computer science, in particular the development of data science as a mode of analysis, sometimes, misinterpreted as a mode of intelligence. In particular, this article will argue that with the advent of forms of hype (sometimes referred to as the hype cycle) in relation to big data and …
An Architectural Design And Evaluation Of An Affective Tutoring System For Novice Programmers, Hua Leong Fwa
An Architectural Design And Evaluation Of An Affective Tutoring System For Novice Programmers, Hua Leong Fwa
Research Collection School Of Computing and Information Systems
Affect is prevalent in learning and it influences students’ learning achievement. This paper details the design and evaluation of an Affective Tutoring System (ATS) that tutors student in computer programming. Although most ATSs are purpose built for a specific domain, making adaptation to another domain difficult, this ATS is architected for adaptability and extensibility. This study also addresses a lack of research exploring the theories and methods of integrating affect and learning within the learning process by proposing methods of regulating the negative affect of students. Both quantitative and qualitative techniques were used for evaluation of the effectiveness of the …
Exploring Online Novelty Detection Using First Story Detection Models, Fei Wang, Robert J. Ross, John D. Kelleher
Exploring Online Novelty Detection Using First Story Detection Models, Fei Wang, Robert J. Ross, John D. Kelleher
Conference papers
Online novelty detection is an important technology in understanding and exploiting streaming data. One application of online novelty detection is First Story Detection (FSD) which attempts to find the very first story about a new topic, e.g. the first news report discussing the “Beast from the East” hitting Ireland. Although hundreds of FSD models have been developed, the vast majority of these only aim at improving the performance of the detection for some specific dataset, and very few focus on the insight of novelty itself. We believe that online novelty detection, framed as an unsupervised learning problem, always requires a …
A Multi-Task Approach To Incremental Dialogue State Tracking, Anh Duong Trinh, Robert J. Ross, John D. Kelleher
A Multi-Task Approach To Incremental Dialogue State Tracking, Anh Duong Trinh, Robert J. Ross, John D. Kelleher
Conference papers
Incrementality is a fundamental feature of language in real world use. To this point, however, the vast majority of work in automated dialogue processing has focused on language as turn based. In this paper we explore the challenge of incremental dialogue state tracking through the development and analysis of a multi-task approach to incremental dialogue state tracking. We present the design of our incremental dialogue state tracker in detail and provide evaluation against the well known Dialogue State Tracking Challenge 2 (DSTC2) dataset. In addition to a standard evaluation of the tracker, we also provide an analysis of the Incrementality …
Personalized Microblog Sentiment Classification Via Adversarial Cross-Lingual Learning, Weichao Wang, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang
Personalized Microblog Sentiment Classification Via Adversarial Cross-Lingual Learning, Weichao Wang, Shi Feng, Wei Gao, Daling Wang, Yifei Zhang
Research Collection School Of Computing and Information Systems
Sentiment expression in microblog posts can be affected by user’s personal character, opinion bias, political stance and so on. Most of existing personalized microblog sentiment classification methods suffer from the insufficiency of discriminative tweets for personalization learning. We observed that microblog users have consistent individuality and opinion bias in different languages. Based on this observation, in this paper we propose a novel user-attention-based Convolutional Neural Network (CNN) model with adversarial cross-lingual learning framework. The user attention mechanism is leveraged in CNN model to capture user’s language-specific individuality from the posts. Then the attention-based CNN model is incorporated into a novel …
Symmetries Are Important, Vladik Kreinovich
Symmetries Are Important, Vladik Kreinovich
Departmental Technical Reports (CS)
This short article explains why symmetries are important, and how they influenced many research projects in which I participated.
Improving Multi-Label Emotion Classification Via Sentiment Classification With Dual Attention Transfer Network, Jianfei Yu, Luis Marujo, Jing Jiang, Pradeep Karuturi, William Brendel
Improving Multi-Label Emotion Classification Via Sentiment Classification With Dual Attention Transfer Network, Jianfei Yu, Luis Marujo, Jing Jiang, Pradeep Karuturi, William Brendel
Research Collection School Of Computing and Information Systems
In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. Extensive experimental results demonstrate that our transfer learning approach can outperform several strong baselines and achieve the state-of-the-art performance on two benchmark datasets.
An Interpretable Neural Fuzzy Inference System For Predictions Of Underpricing In Initial Public Offerings, Di Wang, Xiaolin Qian, Chai Quek, Ah-Hwee Tan, Chunyan Miao, Xiaofeng Zhang, Geok See Ng, You Zhou
An Interpretable Neural Fuzzy Inference System For Predictions Of Underpricing In Initial Public Offerings, Di Wang, Xiaolin Qian, Chai Quek, Ah-Hwee Tan, Chunyan Miao, Xiaofeng Zhang, Geok See Ng, You Zhou
Research Collection School Of Computing and Information Systems
Due to their aptitude in both accurate data processing and human comprehensible reasoning, neural fuzzy inference systems have been widely adopted in various application domains as decision support systems. Especially in real-world scenarios such as decision making in financial transactions, the human experts may be more interested in knowing the comprehensive reasons of certain advices provided by a decision support system in addition to how confident the system is on such advices. In this paper, we apply an integrated autonomous computational model termed genetic algorithm and rough set incorporated neural fuzzy inference system (GARSINFIS) to predict underpricing in initial public …
Joint Representation Learning Of Cross-Lingual Words And Entities Via Attentive Distant Supervision, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Chengjiang Li, Xu Chen, Tiansi Dong
Joint Representation Learning Of Cross-Lingual Words And Entities Via Attentive Distant Supervision, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Chengjiang Li, Xu Chen, Tiansi Dong
Research Collection School Of Computing and Information Systems
Joint representation learning of words and entities benefits many NLP tasks, but has not been well explored in cross-lingual settings. In this paper, we propose a novel method for joint representation learning of cross-lingual words and entities. It captures mutually complementary knowledge, and enables cross-lingual inferences among knowledge bases and texts. Our method does not require parallel corpora, and automatically generates comparable data via distant supervision using multi-lingual knowledge bases. We utilize two types of regularizers to align cross-lingual words and entities, and design knowledge attention and crosslingual attention to further reduce noises. We conducted a series of experiments on …
Learning Generalized Video Memory For Automatic Video Captioning, Poo-Hee Chang, Ah-Hwee Tan
Learning Generalized Video Memory For Automatic Video Captioning, Poo-Hee Chang, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Recent video captioning methods have made great progress by deep learning approaches with convolutional neural networks (CNN) and recurrent neural networks (RNN). While there are techniques that use memory networks for sentence decoding, few work has leveraged on the memory component to learn and generalize the temporal structure in video. In this paper, we propose a new method, namely Generalized Video Memory (GVM), utilizing a memory model for enhancing video description generation. Based on a class of self-organizing neural networks, GVM’s model is able to learn new video features incrementally. The learned generalized memory is further exploited to decode the …
Gogo: An Improved Algorithm To Measure The Semantic Similarity Between Gene Ontology Terms, Chenguang Zhao, Zheng Wang
Gogo: An Improved Algorithm To Measure The Semantic Similarity Between Gene Ontology Terms, Chenguang Zhao, Zheng Wang
Student Publications
Measuring the semantic similarity between Gene Ontology (GO) terms is an essential step in functional bioinformatics research. We implemented a software named GOGO for calculating the semantic similarity between GO terms. GOGO has the advantages of both information-content-based and hybrid methods, such as Resnik’s and Wang’s methods. Moreover, GOGO is relatively fast and does not need to calculate information content (IC) from a large gene annotation corpus but still has the advantage of using IC. This is achieved by considering the number of children nodes in the GO directed acyclic graphs when calculating the semantic contribution of an ancestor node …
Psychological Behavior Of English Learners Utilizing A Cognitive Tutor In An Online Pre-Calculus, Julian Viera Jr., Olga Kosheleva, Vladik Kreinovich
Psychological Behavior Of English Learners Utilizing A Cognitive Tutor In An Online Pre-Calculus, Julian Viera Jr., Olga Kosheleva, Vladik Kreinovich
Departmental Technical Reports (CS)
The educational landscape is becoming a digital learning environment. Students in today's digital world draw from multiple sources of information; from hypertext, videos, social media, to video games and internet searches. English Learners, individuals learning two languages at once, who use software written in English have a passive relationship with the computer when software is not in their native language. They feel that this educational software belongs to another culture. This paper will present findings from a study with English Learners' engagement in a fully online pre-calculus course. The authors utilized Cultural-Historical Activity Theory to describe how English Learners' created …
Lecture: Intro To Data Science - Ml 3 - Week Eleven, Grant Long, Nyc Tech-In-Residence Corps
Lecture: Intro To Data Science - Ml 3 - Week Eleven, Grant Long, Nyc Tech-In-Residence Corps
Open Educational Resources
Lecture for the course "CSC 59970: Intro to Data Science" (Week Eleven) delivered at the City College of New York in Fall 2018 by Grant Long as part of the Tech-in-Residence Corps program.
Predicting Visual Context For Unsupervised Event Segmentation In Continuous Photo-Streams, Ana García Del Molino, Joo-Hwee Lim, Ah-Hwee Tan
Predicting Visual Context For Unsupervised Event Segmentation In Continuous Photo-Streams, Ana García Del Molino, Joo-Hwee Lim, Ah-Hwee Tan
Research Collection School Of Computing and Information Systems
Segmenting video content into events provides semantic structures for indexing, retrieval, and summarization. Since motion cues are not available in continuous photo-streams, and annotations in lifelogging are scarce and costly, the frames are usually clustered into events by comparing the visual features between them in an unsupervised way. However, such methodologies are ineffective to deal with heterogeneous events, e.g. taking a walk, and temporary changes in the sight direction, e.g. at a meeting. To address these limitations, we propose Contextual Event Segmentation (CES), a novel segmentation paradigm that uses an LSTM-based generative network to model the photo-stream sequences, predict their …
Automating Intention Mining, Qiao Huang, Xin Xia, David Lo, Gail C. Murphy
Automating Intention Mining, Qiao Huang, Xin Xia, David Lo, Gail C. Murphy
Research Collection School Of Computing and Information Systems
Developers frequently discuss aspects of the systems they are developing online. The comments they post to discussions form a rich information source about the system. Intention mining, a process introduced by Di Sorbo et al., classifies sentences in developer discussions to enable further analysis. As one example of use, intention mining has been used to help build various recommenders for software developers. The technique introduced by Di Sorbo et al. to categorize sentences is based on linguistic patterns derived from two projects. The limited number of data sources used in this earlier work introduces questions about the comprehensiveness of intention …
Deep Understanding Of Cooking Procedure For Cross-Modal Recipe Retrieval, Jingjing Chen, Chong-Wah Ngo, Fu-Li Feng, Tat-Seng Chua
Deep Understanding Of Cooking Procedure For Cross-Modal Recipe Retrieval, Jingjing Chen, Chong-Wah Ngo, Fu-Li Feng, Tat-Seng Chua
Research Collection School Of Computing and Information Systems
Finding a right recipe that describes the cooking procedure for a dish from just one picture is inherently a difficult problem. Food preparation undergoes a complex process involving raw ingredients, utensils, cutting and cooking operations. This process gives clues to the multimedia presentation of a dish (e.g., taste, colour, shape). However, the description of the process is implicit, implying only the cause of dish presentation rather than the visual effect that can be vividly observed on a picture. Therefore, different from other cross-modal retrieval problems in the literature, recipe search requires the understanding of textually described procedure to predict its …
Interfacing Iconicity - Addressing Software Divarication Through Diagrammatic Design Principles, George Akhvlediani
Interfacing Iconicity - Addressing Software Divarication Through Diagrammatic Design Principles, George Akhvlediani
Theses and Dissertations
This research examines conflicts accompanying the proliferation of computer technology and, more specifically, constellations of dependency in the always expanding volume of software, platforms, and the firms/individuals using them. We identify a pervasive phenomenon of “divarication” in the growing variety of progressively specialized systems and system roles. As software systems enter new thresholds of sophistication, they effectively aggregate many distinct components and protocols. Consequently, we are confronted with a diverse ecology of stratified and thereby incompatible software systems. Software inherits the limitations and potential flaws of its constituent parts, but unlike physical machinery, it isn’t readily disassembled in instances of …
An Evaluation Of Geotagged Twitter Data During Hurricane Irma Using Sentiment Analysis And Topic Modeling For Disaster Resilience, Ike Robert Vayansky
An Evaluation Of Geotagged Twitter Data During Hurricane Irma Using Sentiment Analysis And Topic Modeling For Disaster Resilience, Ike Robert Vayansky
Electronic Theses and Dissertations
Disasters require quick response times, thought-out preparations, overall community, and government support. These efforts will ensure prevention of loss of life and reduce possible damages. The United States has been battered by multiple major hurricanes in the recent years and multiple avenues of disaster response efforts were being tested. Hurricane Irma can be recognized as the most popular hurricane in terms of social media attention. Irma made landfall in Florida as a Category 4 storm and preparation measures taken were intensive thus providing a good measure to evaluate in terms of efficacy. The effectiveness of the response methods utilized are …
Categorizing The Content Of Github Readme Files, Gede Artha Azriadi Prana, Christoph Treude, Ferdian Thung, Thushari Atapattu, David Lo
Categorizing The Content Of Github Readme Files, Gede Artha Azriadi Prana, Christoph Treude, Ferdian Thung, Thushari Atapattu, David Lo
Research Collection School Of Computing and Information Systems
README files play an essential role in shaping a developer’s first impression of a software repository and in documenting the software project that the repository hosts. Yet, we lack a systematic understanding of the content of a typical README file as well as tools that can process these files automatically. To close this gap, we conduct a qualitative study involving the manual annotation of 4,226 README file sections from 393 randomly sampled GitHub repositories and we design and evaluate a classifier and a set of features that can categorize these sections automatically. We find that information discussing the ‘What’ and …
Multiperspective Graph-Theoretic Similarity Measure, Dung D. Le, Hady W. Lauw
Multiperspective Graph-Theoretic Similarity Measure, Dung D. Le, Hady W. Lauw
Research Collection School Of Computing and Information Systems
Determining the similarity between two objects is pertinent to many applications. When the basis for similarity is a set of object-to-object relationships, it is natural to rely on graph-theoretic measures. One seminal technique for measuring the structural-context similarity between a pair of graph vertices is SimRank, whose underlying intuition is that two objects are similar if they are connected by similar objects. However, by design, SimRank as well as its variants capture only a single view or perspective of similarity. Meanwhile, in many real-world scenarios, there emerge multiple perspectives of similarity, i.e., two objects may be similar from one perspective, …
From Tag To Protect: A Tag-Driven Policy Recommender System For Image Sharing, Anna Cinzia Squicciarini, Andrea Novelli, Dan Lin, Cornelia Caragea, Haoti Zhong
From Tag To Protect: A Tag-Driven Policy Recommender System For Image Sharing, Anna Cinzia Squicciarini, Andrea Novelli, Dan Lin, Cornelia Caragea, Haoti Zhong
Computer Science Faculty Research & Creative Works
Sharing images on social network sites has become a part of daily routine for more and more online users. However, in face of the considerable number of images shared online, it is not a trivial task for a person to manually configure proper privacy settings for each of the images that he/she uploaded. The lack of proper privacy protection during image sharing could raise many potential privacy breaches of people's private lives that they are not aware of. In this work, we propose a privacy setting recommender system to help people effortlessly set up the privacy settings for their online …
Evaluating Prose Style Transfer With The Bible, Keith Carlson, Allen Riddell, Daniel Rockmore
Evaluating Prose Style Transfer With The Bible, Keith Carlson, Allen Riddell, Daniel Rockmore
Dartmouth Scholarship
In the prose style transfer task a system, provided with text input and a target prose style, produces output which preserves the meaning of the input text but alters the style. These systems require parallel data for evaluation of results and usually make use of parallel data for training. Currently, there are few publicly available corpora for this task. In this work, we identify a high-quality source of aligned, stylistically distinct text in different versions of the Bible. We provide a standardized split, into training, development and testing data, of the public domain versions in our corpus. This corpus is …
Using Chronicling America’S Images To Explore Digitized Historic Newspapers & Imagine Alternative Futures, Elizabeth Lorang, Leen-Kiat Soh
Using Chronicling America’S Images To Explore Digitized Historic Newspapers & Imagine Alternative Futures, Elizabeth Lorang, Leen-Kiat Soh
University of Nebraska-Lincoln Libraries: Presentations
This presentation situates the work of the Aida team broadly as well as hinges this work on some very specific challenges for digital libraries. In doing so demonstrate the many types of questions and domains to be explored in digitized newspapers.
Tourism Review Sentiment Classification Using A Bidirectional Recurrent Neural Network With An Attention Mechanism And Topic-Enriched Word Vectors, Qin Li, Shaobo Li, Jie Hu, Sen Zhang, Jianjun Hu
Tourism Review Sentiment Classification Using A Bidirectional Recurrent Neural Network With An Attention Mechanism And Topic-Enriched Word Vectors, Qin Li, Shaobo Li, Jie Hu, Sen Zhang, Jianjun Hu
Faculty Publications
Sentiment analysis of online tourist reviews is playing an increasingly important role in tourism. Accurately capturing the attitudes of tourists regarding different aspects of the scenic sites or the overall polarity of their online reviews is key to tourism analysis and application. However, the performances of current document sentiment analysis methods are not satisfactory as they either neglect the topics of the document or do not consider that not all words contribute equally to the meaning of the text. In this work, we propose a bidirectional gated recurrent unit neural network model (BiGRULA) for sentiment analysis by combining a topic …
Building Classifiers With Gmdh For Health Social Networks (Bd Askapatient), John Cardiff, Liliya Akhtyamova, Mikhail Alexandrov
Building Classifiers With Gmdh For Health Social Networks (Bd Askapatient), John Cardiff, Liliya Akhtyamova, Mikhail Alexandrov
Conference Papers
Health social media offer useful data for patients and doctors concerning both various medicines and treatments. Usually, these data are accompanied by their assessments in 5- star scale. But such a detail classification has small usefulness because patients and doctors, first of all, want to know about negative cases and to study in detail the extreme ones. In the paper we build classifiers of texts just for these cases using combined classes as negative, all others and worst, satisfactory, best. For this, we study possibilities of different GMDH-based algorithms and compare them with the results of other methods. The selection …
Entity-Grounded Image Captioning, Annika Lindh, Robert J. Ross, John D. Kelleher
Entity-Grounded Image Captioning, Annika Lindh, Robert J. Ross, John D. Kelleher
Conference papers
An urgent limitation in current Image Captioning models is their tendency to produce generic captions that avoid the interesting detail which makes each image unique. To address this limitation, we propose an approach that enforces a stronger alignment between image regions and specific segments of text. The model architecture is composed of a visual region proposer, a region-order planner and a region-guided caption generator. The region-guided caption generator incorporates a novel information gate which allows visual and textual input of different frequencies and dimensionalities in a Recurrent Neural Network.
Monitoring Scene Understanders With Conceptual Primitive Decomposition And Commonsense Knowledge, Leilani H. Gilpin, Jamie C. Macbeth, Evelyn Florentine
Monitoring Scene Understanders With Conceptual Primitive Decomposition And Commonsense Knowledge, Leilani H. Gilpin, Jamie C. Macbeth, Evelyn Florentine
Computer Science: Faculty Publications
Although there have been many key advancements in connecting text and perception, computer- generated image captions still lack common sense. As a first step towards constraining these perception mechanisms to commonsense judgment, we have developed reasonableness monitors: a wrapper interface that can explain if the descriptive output of an opaque deep neural network is plausible. These monitor a standalone system that uses careful dependency tracking, commonsense knowledge, and conceptual primitives to explain a perceived scene description to be reasonable or not. If such an explanation cannot be made, it is evidence that something unreasonable has been perceived. The development of …
Domain-Specific Knowledge Exploration With Ontology Hierarchical Re-Ranking And Adaptive Learning And Extension, Grace G. Zhao
Domain-Specific Knowledge Exploration With Ontology Hierarchical Re-Ranking And Adaptive Learning And Extension, Grace G. Zhao
Dissertations, Theses, and Capstone Projects
The goal of this research project is the realization of an artificial intelligence-driven lightweight domain knowledge search framework that returns a domain knowledge structure upon request with highly relevant web resources via a set of domain-centric re-ranking algorithms and adaptive ontology learning models. The re-ranking algorithm, a necessary mechanism to counter-play the heterogeneity and unstructured nature of web data, uses augmented queries and a hierarchical taxonomic structure to get further insight into the initial search results obtained from credited generic search engines. A semantic weight scale is applied to each node in the ontology graph and in turn generates a …
Personality Recognition For Deception Detection, Guozhen An
Personality Recognition For Deception Detection, Guozhen An
Dissertations, Theses, and Capstone Projects
Personality aims at capturing stable individual characteristics, typically measurable in quantitative terms, that explain and predict observable behavioral differences. Personality has been proved to be very useful in many life outcomes, and there has been huge interests on predicting personality automatically. Previously, there are tremendous amount of approaches successfully predicting personality. However, most previous research on personality detection has used personality scores assigned by annotators based solely on the text or audio clip, and found that predicting self-reported personality is a much more difficult task than predicting observer-report personality. In our study, we will demonstrate how to accurately detect self-reported …
Fake News Detection: A Deep Learning Approach, Aswini Thota, Priyanka Tilak, Simrat Ahluwalia, Nibrat Lohia
Fake News Detection: A Deep Learning Approach, Aswini Thota, Priyanka Tilak, Simrat Ahluwalia, Nibrat Lohia
SMU Data Science Review
Fake news is defined as a made-up story with an intention to deceive or to mislead. In this paper we present the solution to the task of fake news detection by using Deep Learning architectures. Gartner research [1] predicts that “By 2022, most people in mature economies will consume more false information than true information”. The exponential increase in production and distribution of inaccurate news presents an immediate need for automatically tagging and detecting such twisted news articles. However, automated detection of fake news is a hard task to accomplish as it requires the model to understand nuances in natural …