Using social media data to understand mobile customer experience and behavior

Understanding mobile customer experience and behavior is an important task for cellular service providers to improve the satisfaction of their customers. To that end, cellular service providers regularly measure the properties of their mobile network, such as signal strength, dropped calls, call blockage, and radio interface failures (RIFs). In addition to these passive measurements collected within the network, understanding customer sentiment from direct customer feedback is also an important means of evaluating user experience. Customers have varied perceptions of mobile network quality, and also react differently to advertising, news articles, and the introduction of new equipment and services. Traditional methods used to assess customer sentiment include direct surveys and mining the transcripts of calls made to customer care centers. Along with this feedback provided directly to the service providers, the rise in social media potentially presents new opportunities to gain further insight into customers by mining public social media data as well. According to a note from one of the largest online social network (OSN) sites in the US [7], as of September 2010 there are 175 million registered users, and 95 million text messages communicated among users per day. Additionally, many OSNs provide APIs to retrieve publically available message data, which can be used to collect this data for analysis and interpretation. Our plan is to correlate different sources of measurements and user feedback to understand the social media usage patterns from mobile data users in a large nationwide cellular network. In particular, we are interested in quantifying the traffic volume, the growing trend of social media usage and how it interacts with traditional communication channels, such as voice calls, text messaging, etc. In addition, we are interested in detecting interesting network events from users' communication on OSN sites and studying the temporal aspects - how the various types of user feedback behave with respect to timing. We develop a novel approach which combines burst detection and text mining to detect emerging issues from online messages on a large OSN network. Through a case study, our method shows promising results in identifying a burst of activities using the OSN feedback, whereas customer care notes exhibit noticeable delays in detecting such an event which may lead to unnecessary operational expenses. --Mobile customer experience,social media,text data mining,customer feedback

Detecting spatiotemporal clusters of accidental poisoning mortality among Texas counties, U.S., 1980 – 2001

BACKGROUND: Accidental poisoning is one of the leading causes of injury in the United States, second only to motor vehicle accidents. According to the Centers for Disease Control and Prevention, the rates of accidental poisoning mortality have been increasing in the past fourteen years nationally. In Texas, mortality rates from accidental poisoning have mirrored national trends, increasing linearly from 1981 to 2001. The purpose of this study was to determine if there are spatiotemporal clusters of accidental poisoning mortality among Texas counties, and if so, whether there are variations in clustering and risk according to gender and race/ethnicity. The Spatial Scan Statistic in combination with GIS software was used to identify potential clusters between 1980 and 2001 among Texas counties, and Poisson regression was used to evaluate risk differences. RESULTS: Several significant (p < 0.05) accidental poisoning mortality clusters were identified in different regions of Texas. The geographic and temporal persistence of clusters was found to vary by racial group, gender, and race/gender combinations, and most of the clusters persisted into the present decade. Poisson regression revealed significant differences in risk according to race and gender. The Black population was found to be at greatest risk of accidental poisoning mortality relative to other race/ethnic groups (Relative Risk (RR) = 1.25, 95% Confidence Interval (CI) = 1.24 – 1.27), and the male population was found to be at elevated risk (RR = 2.47, 95% CI = 2.45 – 2.50) when the female population was used as a reference. CONCLUSION: The findings of the present study provide evidence for the existence of accidental poisoning mortality clusters in Texas, demonstrate the persistence of these clusters into the present decade, and show the spatiotemporal variations in risk and clustering of accidental poisoning deaths by gender and race/ethnicity. By quantifying disparities in accidental poisoning mortality by place, time and person, this study demonstrates the utility of the spatial scan statistic combined with GIS and regression methods in identifying priority areas for public health planning and resource allocation

A Study of Military Recruitment Strategies for Dentists: Possible Implications for Academia

Results of the annual American Dental Education Association surveys of dental school seniors show approximately 10 percent of graduates enter federal government services while less than 1 percent enter dental academia. To examine this difference, this study sought the perceptions of senior dental students and junior military dental officers regarding their choice of a military career in order to determine how military recruitment strategies influenced their career decisions. Official documents explaining military recruitment efforts were requested from the military services and summarized. In-depth telephone interviews were conducted to gather perception data from the students and dental officers on successful strategies. By employing several strategies, the military was able to inform potential recruits about the benefits of being a dentist in the military. The opportunity to have the military finance a student's dental education was a successful military recruitment tool. Other enticing factors included guaranteed employment upon graduation, prestige associated with serving in the military, access to postgraduate training, minimal practice management responsibilities, and opportunities to continue learning and improve clinical skills without significant financial implications. It was concluded that dental education can use the same strategies to highlight the benefits of an academic career and offer many similar incentives that may encourage students to consider a career path in dental education

miRNAMap: genomic maps of microRNA genes and their target genes in mammalian genomes

Recent work has demonstrated that microRNAs (miRNAs) are involved in critical biological processes by suppressing the translation of coding genes. This work develops an integrated database, miRNAMap, to store the known miRNA genes, the putative miRNA genes, the known miRNA targets and the putative miRNA targets. The known miRNA genes in four mammalian genomes such as human, mouse, rat and dog are obtained from miRBase, and experimentally validated miRNA targets are identified in a survey of the literature. Putative miRNA precursors were identified by RNAz, which is a non-coding RNA prediction tool based on comparative sequence analysis. The mature miRNA of the putative miRNA genes is accurately determined using a machine learning approach, mmiRNA. Then, miRanda was applied to predict the miRNA targets within the conserved regions in 3′-UTR of the genes in the four mammalian genomes. The miRNAMap also provides the expression profiles of the known miRNAs, cross-species comparisons, gene annotations and cross-links to other biological databases. Both textual and graphical web interface are provided to facilitate the retrieval of data from the miRNAMap. The database is freely available at

Bridging Speech and Textual Pre-trained Models with Unsupervised ASR

Spoken language understanding (SLU) is a task aiming to extract high-level semantics from spoken utterances. Previous works have investigated the use of speech self-supervised models and textual pre-trained models, which have shown reasonable improvements to various SLU tasks. However, because of the mismatched modalities between speech signals and text tokens, previous methods usually need complex designs of the frameworks. This work proposes a simple yet efficient unsupervised paradigm that connects speech and textual pre-trained models, resulting in an unsupervised speech-to-semantic pre-trained model for various tasks in SLU. To be specific, we propose to use unsupervised automatic speech recognition (ASR) as a connector that bridges different modalities used in speech and textual pre-trained models. Our experiments show that unsupervised ASR itself can improve the representations from speech self-supervised models. More importantly, it is shown as an efficient connector between speech and textual pre-trained models, improving the performances of five different SLU tasks. Notably, on spoken question answering, we reach the state-of-the-art result over the challenging NMSQA benchmark.Comment: ICASSP2023 submissio