39,567 research outputs found
Social Media Analytics in Disaster Response: A Comprehensive Review
Social media has emerged as a valuable resource for disaster management,
revolutionizing the way emergency response and recovery efforts are conducted
during natural disasters. This review paper aims to provide a comprehensive
analysis of social media analytics for disaster management. The abstract begins
by highlighting the increasing prevalence of natural disasters and the need for
effective strategies to mitigate their impact. It then emphasizes the growing
influence of social media in disaster situations, discussing its role in
disaster detection, situational awareness, and emergency communication. The
abstract explores the challenges and opportunities associated with leveraging
social media data for disaster management purposes. It examines methodologies
and techniques used in social media analytics, including data collection,
preprocessing, and analysis, with a focus on data mining and machine learning
approaches. The abstract also presents a thorough examination of case studies
and best practices that demonstrate the successful application of social media
analytics in disaster response and recovery. Ethical considerations and privacy
concerns related to the use of social media data in disaster scenarios are
addressed. The abstract concludes by identifying future research directions and
potential advancements in social media analytics for disaster management. The
review paper aims to provide practitioners and researchers with a comprehensive
understanding of the current state of social media analytics in disaster
management, while highlighting the need for continued research and innovation
in this field.Comment: 11 page
Three Essays on Big Data Consumer Analytics in E-Commerce
Consumers are increasingly spending more time and money online. Business
to consumer e-commerce is growing on average of 20 percent each year and
has reached 1.5 trillion dollars globally in 2014. Given the scale and growth
of consumer online purchase and usage data, firms\u27 ability to understand
and utilize this data is becoming an essential competitive strategy.
But, large-scale data analytics in e-commerce is still at its nascent stage and there
is much to be learned in all aspects of e-commerce. Successful analytics on big data often require a combination of both data mining and econometrics: data mining to reduce or structure
(from unstructured data such as text, photo, and video) large-scale data
and econometric analyses to truly understand and assign causality to interesting
patterns. In my dissertation, I study how firms can better utilize big data
analytics and specific applications of machine learning techniques for improved
e-commerce using theory-driven econometrical and experimental studies. I
show that e-commerce managers can now formulate data-driven strategies for
many aspect of business including cross-selling via recommenders on sales
sites to increasing brand awareness and leads via social media content-engineered-marketing.
These results are readily actionable with far-reaching economical consequences
IMPROVING OPINION MINING BY CLASSIFYING FACTS AND OPINIONS IN TWITTER - A DEEP LEARNING APPROACH
The massive social media data presents businesses with an immense opportunity to extract useful insights. However, social media messages typically consist of both facts and opinions, posing a challenge to analytics applications that focus more on either facts or opinions. Distinguishing facts and opinions from social media may significantly improve both, fact seeking applications that aims to capture breaking news, as well as user opinion seeking applications that aims to evaluate users\u27 sentiment towards an event or entity. Despite, the growing need, classifying facts from opinion in social media, has gained minimal attention.
In this study we examine the limitation of applying existing, subjectivity detection methods that identifies subjective contents in textual data. In the context of social media, specifically in microblogs like Twitter, the content is dirty with respect to spelling, syntax, extensive usage of emoticons and abbreviation apart from the overall issue of data sparsity. Traditional methods of checking individual words against a predefined lexicon data set, do not often yield required accuracy for this task. Primary objective of this study is to address this limitation and provide an alternative method to improve this classification task and opinion mining in general.
The study proposes usmg supplemental information from Twitter metadata and empirically demonstrates the improvement in performance. To ensure rigor and relevance, design science research methodology is adopted for this project. We propose a deep learning algorithm that automatically separates facts from opinions in Twitter messages. Our model combines bag-of-word features with selected manually-engineered features from Twitter metadata in a multipm1 experiment. We leverage an external reference dataset to develop our manually-engineered feature variables and evaluated efficiency against three external baseline tools. The study uses eight different machine learning classifiers to demonstrate the robustness of the manual feature set. Next, we combine these manually-engineered features with features extracted from bag-of-words model in our proposed deep learning model. Our algorithm significantly outperformed multiple popular baselines in the internal evaluation pm1 of the experiment.
Next as part of practical usefulness, we illustrated how distinguishing facts and opinions
can be useful in a real world business application. We applied our proposed algorithm to an external opinion mining application that tracks emerging customer complaints from social media conversation. We conducted our case study with three large financial institutions using Twitter data for a period of 16 weeks. The study observed considerable improvement in that external application after integrating our algorithm and concludes that it indeed benefit subsequent analytics applications
Big Data Analytics for Early Detection of Drug Safety Signals in Postmarketing Surveillance
The increasing availability of vast amounts of healthcare data and the advancements in analytics techniques have opened new avenues for drug safety surveillance. This study investigates the application of big data analytics in this domain, focusing on data aggregation, signal detection, real-time monitoring, signal validation and prioritization, comparative effectiveness studies, and data integration and collaboration. It explores how diverse data sources, such as electronic health records (EHRs), insurance claims databases, social media, and patient forums, can be integrated to obtain a comprehensive view of drug usage patterns and potential safety issues. Advanced analytics techniques, including data mining, machine learning, and natural language processing, are examined for their ability to automatically detect potential drug safety signals. The study emphasizes the significance of real-time monitoring for the rapid identification of emerging drug safety issues and the role of signal validation and prioritization in focusing resources on critical signals. Furthermore, it explores how big data analytics enables comparative effectiveness studies to assess the safety profiles of different interventions. The research also highlights the importance of data integration and collaboration in enhancing the understanding of drug safety signals and promoting collective decision-making among stakeholders
Social Media Text Mining Framework for Drug Abuse: An Opioid Crisis Case Analysis
Social media is considered as a promising and viable source of data for gaining insights into various disease conditions, patients’ attitudes and behaviors, and medications. The daily use of social media provides new opportunities for analyzing several aspects of communication. Social media as a big data source can be used to recognize communication and behavioral themes of problematic use of prescription drugs. Mining and analyzing such media have challenges and limitations with respect to topic deduction and data quality. There is a need for a structured approach to efficiently and effectively analyze social media content related to drug abuse in a manner that can mitigate the challenges surrounding the use of this data source.
Following a design science research methodology, the research aims at developing and evaluating a framework for mining and analyzing social media content related to drug abuse in a manner that will mitigate challenges and limitations related to topic deduction and data quality. The framework consists of four phases: Topic Discovery and Detection; Data Collection; Data Preparation and Quality; and Analysis and Results.
The topic discovery and detection phase consists of a topic expansion stage for the drug abuse related topics that address the research domain and objectives. The topic expansion is based on different terms related to keywords, categories, and characteristics of the topic of interest and the objective of monitoring. To formalize the process and supporting artifacts, we create an ontology for drug abuse that captures the different categories that exist in the topic expansion and the literature. The data collection phase is characterized by the date range, social media platforms, search keywords, and a set of inclusion/exclusion criteria. The data preparation and quality phase is mainly concerned with obtaining high-quality data to mitigate problems with data veracity. In this phase, we pre-process the collected data then we evaluate the quality of the data, with respect to the terms and objectives of the research topic phase, using a data quality evaluation matrix. Finally, in the data analysis phase, the researcher can choose the suitable analysis approach. We used a combination of unsupervised and supervised machine learning approaches, including opinion and content analysis modeling.
We demonstrate and evaluate the applicability of the proposed framework to identify common concerns toward opioid crisis from two perspectives; the addicted users’ perspective and the public’s (non-addicted users) perspective. In both cases, data is collected from twitter using Crimson Hexagon, a social media analytics tool for data collection and analysis. Natural language processing is used for data preparation and pre-processing. Different data visualization techniques such as, word clouds and clustering visualization, are used to form a deeper understanding of the relationships among the identified themes for the selected communities. The results help in understanding concerns of the public and opioid addicts towards the opioid crisis in the United States. Results of this study could help in understanding the problem aspects and provide key input when it comes to defining and implementing innovative solutions/strategies to face the opioid epidemic.
From a theoretical perspective, this study highlights the importance of developing and adapting text mining techniques to social media for drug abuse. This study proposes a social media text mining framework for drug abuse research which lead to a good quality of datasets. Emphasis is placed on developing methods for improving the discovery and identification of topics in social media domains characterized by a plethora of highly diverse terms and a lack of commonly available dictionary/language by the community such as in the opioid and drug abuse case. From a practical perspective, automatically analyzing social media users’ posts using machine learning tools can help in understanding the public themes and topics that exist in the recent discussions of online users of social media networks. This could help in developing proper mitigation strategies. Examples of such strategies can be gaining insights from the discussion topics to make the opioid media campaigns more effective in preventing opioid misuse. Finally, the study helps address some of the U.S. Department of Health and Human Services (HHS) five-point strategy by providing a systematic approach that could support conducting better research on addiction and drug abuse and strengthening public health data reporting and collection using social media data
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