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What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding

Ugur Kursuncu, Manas Gaur, Usha Lokala, Anurag Illendula, Krishnaprasad Thirunarayan, Raminta Daniulaityte, Amit Sheth, I. Budak Arpinar
2018 2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)  
With 93% of pro-marijuana population in US favoring legalization of medical marijuana 1 , high expectations of a greater return for Marijuana stocks 2 , and public actively sharing information about medical  ...  Identification and characterization of different user types can allow us to conduct more fine-grained spatiotemporal analysis to identify dominant or emerging topics in the echo chambers of marijuana-related  ...  Fig 1 captures the word cloud synthesized using the tweet content of users pertaining to Informed Agency user type that can be used to glean related topics.  ... 
doi:10.1109/wi.2018.00-50 dblp:conf/webi/KursuncuGLITDSA18 fatcat:uppbpagtajc7bb47223r264wae

"What's ur type?" Contextualized Classification of User Types in Marijuana-related Communications using Compositional Multiview Embedding [article]

Ugur Kursuncu, Manas Gaur, Usha Lokala, Anurag Illendula, Krishnaprasad Thirunarayan, Raminta Daniulaityte, Amit Sheth, I. Budak Arpinar
2018 arXiv   pre-print
With 93% of pro-marijuana population in US favoring legalization of medical marijuana, high expectations of a greater return for Marijuana stocks, and public actively sharing information about medical,  ...  Identification and characterization of different user types can allow us to conduct more fine-grained spatiotemporal analysis to identify dominant or emerging topics in the echo chambers of marijuana-related  ...  The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH.  ... 
arXiv:1806.06813v1 fatcat:fc3vokveyvdpnpjc2ejcaihu4a

Text analytics in industry: Challenges, desiderata and trends

Ashwin Ittoo, Le Minh Nguyen, Antal van den Bosch
2016 Computers in industry (Print)  
Evaluation: describing the experiments performed to assess the performance of the proposed techniques.  ...  Another recent development in the NLP field is to improve NLP or text analytics systems by using so-called word embeddings, distributed word representation produced by dimension-reduction techniques most  ...  Antal van den Bosch (Ph.D. 1997, Universiteit Maastricht) is professor of language and speech technology at the Centre for Language Studies at Radboud University, Nijmegen, the Netherlands.  ... 
doi:10.1016/j.compind.2015.12.001 fatcat:3cp4p55qn5bincjfr4jidqs6ie

Machine-learning methods for identifying social media-based requests for urgent help during hurricanes

Ashwin Devaraj, Dhiraj Murthy, Aman Dontula
2020 International Journal of Disaster Risk Reduction  
We highlight the utility of average word embeddings for training non-neural models, and that such features produce results competitive with 20 more traditional n-gram and POS features.  ...  We find that the best-performing classifiers are a convolutional neural network (CNN) trained on word embeddings, 15 support vector machine (SVM) trained on average word embeddings, and multilayer perceptron  ...  For our models, we use 100-dimensional GloVe word embeddings pretrained on a corpus of 2 billion tweets [20] .  ... 
doi:10.1016/j.ijdrr.2020.101757 fatcat:s7cyfg6exbdejpx7rydxku3eiy

Lies Kill, Facts Save: Detecting COVID-19 Misinformation in Twitter

Mabrook S. Al-Rakhami, Atif M. Al-Amri
2020 IEEE Access  
Online social networks (ONSs) such as Twitter have grown to be very useful tools for the dissemination of information.  ...  For our analysis, we propose an ensemble-learning-based framework for verifying the credibility of a vast number of tweets.  ...  ACKNOWLEDGMENT The authors thank the Deanship of Scientific Research and RSSU at King Saud University for their technical support.  ... 
doi:10.1109/access.2020.3019600 pmid:34192115 pmcid:PMC8043503 fatcat:vg52c7qthvdmvhddsdd2e7koym

Mining Twitter to Assess the Public Perception of the "Internet of Things"

Jiang Bian, Kenji Yoshigoe, Amanda Hicks, Jiawei Yuan, Zhe He, Mengjun Xie, Yi Guo, Mattia Prosperi, Ramzi Salloum, François Modave, Lixia Yao
2016 PLoS ONE  
Our analysis was challenged by the limited fraction of tweets relevant to our study.  ...  In this paper, we have mined Twitter to understand the public's perception of the Internet of Things (IoT).  ...  Acknowledgments This work was supported in part by the NIH/NCATS Clinical and Translational Science Awards to the University of Florida UL1TR001427.  ... 
doi:10.1371/journal.pone.0158450 pmid:27391760 pmcid:PMC4938510 fatcat:y7tgbbhcnrdvlidm4k44luz65y

Detection of Suicide Ideation in Social Media Forums Using Deep Learning

Michael Mesfin Tadesse, Hongfei Lin, Bo Xu, Liang Yang
2019 Algorithms  
Our experiment shows the combined neural network architecture with word embedding techniques can achieve the best relevance classification results.  ...  Additionally, our results support the strength and ability of deep learning architectures to build an effective model for a suicide risk assessment in various text classification tasks.  ...  Acknowledgments: The authors would like to thank Janka Koperdanova for her full support and editing of the paper. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/a13010007 fatcat:7w2sevyfejfobdltrzibwkl4oq

Review article: Detection of actionable tweets in crisis events

Anna Kruspe, Jens Kersten, Friederike Klan
2021 Natural Hazards and Earth System Sciences  
., official news) and can offer personal perspectives on events, such as opinions or specific needs. In the future, these messages can also serve to assess disaster risks.  ...  In this review article, we present methods for the automatic detection of crisis-related messages (tweets) on Twitter.  ...  A quite interesting and effective approach lies in directly using word or sentence embeddings to semantically cluster tweets for various tasks, like the detection of topics (de Miranda et al., 2020) ,  ... 
doi:10.5194/nhess-21-1825-2021 fatcat:3npxsdoo2zf5hko4o6mwbesfzu

Predictive Analysis on Twitter: Techniques and Applications [chapter]

Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, I. Budak Arpinar
2018 Lecture Notes in Social Networks  
In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data.  ...  Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking  ...  Training the word embedding model on a problem-specific corpus is essential for high-quality domain-specific applications, since the neighborhood set of words for an input term impacts its word embedding  ... 
doi:10.1007/978-3-319-94105-9_4 fatcat:knquzcuqcjdjjguzq435nq5kni

Predictive Analysis on Twitter: Techniques and Applications [article]

Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, I. Budak Arpinar
2018 arXiv   pre-print
In this chapter, we discuss techniques, approaches and state-of-the-art applications of predictive analysis of Twitter data.  ...  Specifically, we present fine-grained analysis involving aspects such as sentiment, emotion, and the use of domain knowledge in the coarse-grained analysis of Twitter data for making decisions and taking  ...  Training the word embedding model on a problem-specific corpus is essential for high-quality domain-specific applications, since the neighborhood set of words for an input term impacts its word embedding  ... 
arXiv:1806.02377v1 fatcat:gm5cqpmgvfggzgxgzocv4c3fqi

Information extraction from digital social trace data with applications to social media and scholarly communication data

Shubhanshu Mishra
2020 SIGIR Forum  
This identification allows us to utilize the graph structure of the data (e.g., user connected to a tweet, author connected to a paper, author connected to authors, etc.) for developing new information  ...  The thesis aims to act as a bridge between research questions and techniques used in DSTD from different domains.  ...  The table also shows that for person, product, movie, and tvshow the top features were specific dimensions of the pre-trained word embedding.  ... 
doi:10.1145/3451964.3451981 fatcat:36djwlckprhl5hymzhivrbnscu

Towards Explainable Fact Checking [article]

Isabelle Augenstein
2021 arXiv   pre-print
This development has spurred research in the area of automatic fact checking, from approaches to detect check-worthy claims and determining the stance of tweets towards claims, to methods to determine  ...  At the same time, it is desirable to explain how they arrive at certain decisions, especially if they are to be used for decision making.  ...  • Word Embeddings: we use Word2Vec Mikolov et al. 2013b to represent the textual content of each tweet.  ... 
arXiv:2108.10274v2 fatcat:5s4an6irezcjfmvvhmiaeqarh4

Mining social media data for biomedical signals and health-related behavior [article]

Rion Brattig Correia and Ian B. Wood and Johan Bollen and Luis M. Rocha
2020 arXiv   pre-print
We also discuss a variety of innovative uses of social media data for health-related applications and important limitations in social media data access and use.  ...  with a variety of health conditions and medical treatments.  ...  The authors would like to thank Deborah Rocha for editing this review and Marijn ten  ... 
arXiv:2001.10285v1 fatcat:rbpjmvltkjderdrbzz4midfxey

Curating Social Media Data [article]

Kushal Vaghani
2020 arXiv   pre-print
For the purposes of this research, we use Twitter as our motivational social media data platform due to its popularity.  ...  The implementation of this pipeline also includes a set of tools for automatically creating micro-tasks to facilitate the contribution of crowd users in curating the raw data.  ...  Tweet "c" consists of the word bypass, which is a medical term for a surgery or a heart surgery. proper cleansing and curation of data for robust analytics.  ... 
arXiv:2002.09202v1 fatcat:5w2coglezfc4jlnu4h6oh23oqm

Emotion AI-Driven Sentiment Analysis: A Survey, Future Research Directions, and Open Issues

Chakriswaran, Vincent, Srinivasan, Sharma, Chang, Reina
2019 Applied Sciences  
The essential use of natural language processing is to analyze the sentiment of the author via the context.  ...  In addition, this paper offers a review of ontology-based SA and lexicon-based SA along with machine learning models that are used to analyze the sentiment of the given context.  ...  .  Emotion identification (sarcasm)-it is difficult for the machine to identify sarcastic statements.  ... 
doi:10.3390/app9245462 fatcat:qhr2pjyovzdwjhmrfs6toyhz2m
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