Browsing by Author "Li, Liuqing"

Now showing 1 - 6 of 6
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    Applying GIS and Text Mining Methods to Twitter Data to Explore the Spatiotemporal Patterns of Topics of Interest in Kuwait
    G. Almatar, Muhammad; Alazmi, Huda S.; Li, Liuqing; Fox, Edward A. (MDPI, 2020-11-25)
    Researchers have developed various approaches for exploring the spatial information, temporal patterns, and Twitter content in topics of interest in order to generate a better understanding of human behavior; however, few investigations have integrated these three dimensions simultaneously. This study analyzes the content of tweets in order to conduct a spatiotemporal exploration of the main topics of interest in Kuwait in order to provide a deeper understanding of the topics people think about, when they think about them, and where they tweet about them. To this end, we collect, process, and analyze tweets from nearly 120 areas in Kuwait over a 10-month period. The study’s results indicate that religion, emotions, education, and public policy are the most popular topics of interest in Kuwait. Regarding the spatiotemporal analysis, people post more tweets regarding religion on Fridays, a holy day for Muslims in Kuwait. Moreover, people are more likely to tweet about policy and education on weekdays rather than weekends. In contrast, people tweet about emotional expressions more often on weekends. From the spatial perspectives, spatial clustering in topics occurs across the days of the week. The findings are applicable to further topic analysis and similar research in other countries.
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    CS5604 Fall 2016 Solr Team Project Report
    Li, Liuqing; Pillai, Anusha; Wang, Ye; Tian, Ke (Virginia Tech, 2016-12-07)
    This submission describes the work the SOLR team completed in Fall 2016. It includes the final report and presentation, as well as key relevant materials (indexing scripts & Java code). Based on the work in Spring 2016, the SOLR team improved the general search infrastructure supporting the IDEAL and GETAR projects, both funded by NSF. The main responsibility was to configure the Basic Indexing and Incremental Indexing (Near Real Time, NRT Indexing) for tweets and web page collections in DLRL's Hadoop Cluster. The goal of Basic Indexing was to index the big collection that contains more than 1.2 billion tweets. The idea of NRT Indexing was to monitor real-time changes in HBase and update the Solr results as appropriate. The main motivation behind the Custom Ranking was to design and implement a new scoring function to re-rank the retrieved results in Solr. Based on the text similarity, a basic document recommender was also created to retrieve the similar documents related to a specific one. Finally, new well written manuals could be easier for users and developers to read and get familiar with Solr and its relevant tools. Throughout the semester we closely collaborated with the Collection Management Tweets (CMT), Collection Management Webpages (CMW), Classification (CLA), Clustering and Topic Analysis (CTA), and Front-End (FE) teams in getting requirements, input data, and suggestions for data visualization.
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    CS6604 Spring 2017 Global Events Team Project
    Li, Liuqing; Harb, Islam; Galad, Andrej (Virginia Tech, 2017-05-03)
    This submission describes the work the Global Events team completed in Spring 2017. It includes the final report and presentation, as well as key relevant materials (source code). Based on the previous reports and different modules created by former teams, the Global Events team established a pipeline for processing Web ARChives supporting the IDEAL and GETAR projects, both funded by NSF. With the Internet Archive’s help, the Global Events team enhanced the Event Focused Crawler to retrieve more relevant webpages (i.e., about school shooting events) in WARC format. ArchiveSpark, an Apache Spark framework that facilitates access to Web Archives, was deployed on a stand-alone server, and multiple techniques, such as parsing, Stanford NER, regular expression and statistical methods, were leveraged to process and analyze the data, and describe those events. For the data visualization, an integrated user interface using Gradle was designed and implemented for trend results, which can be easily used by both CS and non-CS researchers and students. Moreover, new well written manuals could be easier for users and developers to read and get familiar with ArchiveSpark, Spark, and Scala.
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    Event-related Collections Understanding and Services
    Li, Liuqing (Virginia Tech, 2020-03-18)
    Event-related collections, including both tweets and webpages, have valuable information, and are worth exploring in interdisciplinary research and education. Unfortunately, such data is noisy, so this variety of information has not been adequately exploited. Further, for better understanding, more knowledge hidden behind events needs to be unearthed. Regarding these collections, different societies may have different requirements in particular scenarios. Some may need relatively clean datasets for data exploration and data mining. Social researchers require preprocessing of information, so they can conduct analyses. General societies are interested in the overall descriptions of events. However, few systems, tools, or methods exist to support the flexible use of event-related collections. In this research, we propose a new, integrated system to process and analyze event-related collections at different levels (i.e., data, information, and knowledge). It also provides various services and covers the most important stages in a system pipeline, including collection development, curation, analysis, integration, and visualization. Firstly, we propose a query likelihood model with pre-query design and post-query expansion to rank a webpage corpus by query generation probability, and retrieve relevant webpages from event-related tweet collections. We further preserve webpage data into WARC files and enrich original tweets with webpages in JSON format. As an application of data management, we conduct an empirical study of the embedded URLs in tweets based on collection development and data curation techniques. Secondly, we develop TwiRole, an integrated model for 3-way user classification on Twitter, which detects brand-related, female-related, and male-related tweeters through multiple features with both machine learning (i.e., random forest classifier) and deep learning (i.e., an 18-layer ResNet) techniques. As guidance to user-centered social research at the information level, we combine TwiRole with a pre-trained recurrent neural network-based emotion detection model, and carry out tweeting pattern analyses on disaster-related collections. Finally, we propose a tweet-guided multi-document summarization (TMDS) model, which generates summaries of the event-related collections by using tweets associated with those events. The TMDS model also considers three aspects of named entities (i.e., importance, relatedness, and diversity) as well as topics, to score sentences in webpages, and then rank selected relevant sentences in proper order for summarization. The entire system is realized using many technologies, such as collection development, natural language processing, machine learning, and deep learning. For each part, comprehensive evaluations are carried out, that confirm the effectiveness and accuracy of our proposed approaches. Regarding broader impact, the outcomes proposed in our study can be easily adopted or extended for further event analyses and service development.
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    A Hybrid Model for Role-related User Classification on Twitter
    Li, Liuqing; Song, Ziqian; Zhang, Xuan; Fox, Edward A. (Virginia Tech, 2018-11-15)
    To aid a variety of research studies, we propose TWIROLE, a hybrid model for role-related user classification on Twitter, which detects male-related, female-related, and brand-related (i.e., organization or institution) users. TWIROLE leverages features from tweet contents, user profiles, and profile images, and then applies our hybrid model to identify a user’s role. To evaluate it, we used two existing large datasets about Twitter users, and conducted both intra- and inter-comparison experiments. TWIROLE outperforms existing methods and obtains more balanced results over the several roles. We also confirm that user names and profile images are good indicators for this task. Our research extends prior work that does not consider brand-related users, and is an aid to future evaluation efforts relative to investigations that rely upon self-labeled datasets.
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    Teaching Natural Language Processing through Big Data Text Summarization with Problem-Based Learning
    Li, Liuqing; Geissinger, Jack H.; Ingram, William A.; Fox, Edward A. (Sciendo, 2020)
    Natural language processing (NLP) covers a large number of topics and tasks related to data and information management, leading to a complex and challenging teaching process. Meanwhile, problem-based learning is a teaching technique specifically designed to motivate students to learn efficiently, work collaboratively, and communicate effectively. With this aim, we developed a problem-based learning course for both undergraduate and graduate students to teach NLP. We provided student teams with big data sets, basic guidelines, cloud computing resources, and other aids to help different teams in summarizing two types of big collections: Web pages related to events, and electronic theses and dissertations (ETDs). Student teams then deployed different libraries, tools, methods, and algorithms to solve the task of big data text summarization. Summarization is an ideal problem to address learning NLP since it involves all levels of linguistics, as well as many of the tools and techniques used by NLP practitioners. The evaluation results showed that all teams generated coherent and readable summaries. Many summaries were of high quality and accurately described their corresponding events or ETD chapters, and the teams produced them along with NLP pipelines in a single semester. Further, both undergraduate and graduate students gave statistically significant positive feedback, relative to other courses in the Department of Computer Science. Accordingly, we encourage educators in the data and information management field to use our approach or similar methods in their teaching and hope that other researchers will also use our data sets and synergistic solutions to approach the new and challenging tasks we addressed.