Creating Full Individual-level Location Timelines from Sparse Social Media Data

In many domain applications, a continuous timeline of human locations is critical; for example for understanding possible locations where a disease may spread, or the flow of traffic. While data sources such as GPS trackers or Call Data Records are temporally-rich, they are expensive, often not publicly available or garnered only in select locations, restricting their wide use. Conversely, geo-located social media data are publicly and freely available, but present challenges especially for full timeline inference due to their sparse nature. We propose a stochastic framework, Intermediate Location Computing (ILC) which uses prior knowledge about human mobility patterns to predict every missing location from an individual's social media timeline. We compare ILC with a state-of-the-art RNN baseline as well as methods that are optimized for next-location prediction only. For three major cities, ILC predicts the top 1 location for all missing locations in a timeline, at 1 and 2-hour resolution, with up to 77.2% accuracy (up to 6% better accuracy than all compared methods). Specifically, ILC also outperforms the RNN in settings of low data; both cases of very small number of users (under 50), as well as settings with more users, but with sparser timelines. In general, the RNN model needs a higher number of users to achieve the same performance as ILC. Overall, this work illustrates the tradeoff between prior knowledge of heuristics and more data, for an important societal problem of filling in entire timelines using freely available, but sparse social media data.Comment: 10 pages, 8 figures, 2 table

From the User to the Medium: Neural Profiling Across Web Communities

Online communities provide a unique way for individuals to access information from those in similar circumstances, which can be critical for health conditions that require daily and personalized management. As these groups and topics often arise organically, identifying the types of topics discussed is necessary to understand their needs. As well, these communities and people in them can be quite diverse, and existing community detection methods have not been extended towards evaluating these heterogeneities. This has been limited as community detection methodologies have not focused on community detection based on semantic relations between textual features of the user-generated content. Thus here we develop an approach, NeuroCom, that optimally finds dense groups of users as communities in a latent space inferred by neural representation of published contents of users. By embedding of words and messages, we show that NeuroCom demonstrates improved clustering and identifies more nuanced discussion topics in contrast to other common unsupervised learning approaches

Text Mining Methods for Analyzing Online Health Information and Communication

The Internet provides an alternative way to share health information. Specifically, social network systems such as Twitter, Facebook, Reddit, and disease specific online support forums are increasingly being used to share information on health related topics. This could be in the form of personal health information disclosure to seek suggestions or answering other patients\u27 questions based on their history. This social media uptake gives a new angle to improve the current health communication landscape with consumer generated content from social platforms. With these online modes of communication, health providers can offer more immediate support to the people seeking advice. Non-profit organizations and federal agencies can also diffuse preventative information in such networks for better outcomes. Researchers in health communication can mine user generated content on social networks to understand themes and derive insights into patient experiences that may be impractical to glean through traditional surveys. The main difficulty in mining social health data is in separating the signal from the noise. Social data is characterized by informal nature of content, typos, emoticons, tonal variations (e.g. sarcasm), and ambiguities arising from polysemous words, all of which make it difficult in building automated systems for deriving insights from such sources. In this dissertation, we present four efforts to mine health related insights from user generated social data. In the first effort, we build a model to identify marketing tweets on electronic cigarettes (e-cigs) and assess different topics in marketing and non-marketing messages on e-cigs on Twitter. In our next effort, we build ensemble models to classify messages on a mental health forum for triaging posts whose authors need immediate attention from trained moderators to prevent self-harm. The third effort deals with models from our participation in a shared task on identifying tweets that discuss adverse drug reactions and those that mention medication intake. In the final task, we build a classifier that identifies whether a particular tweet about the popular Juul e-cig indicates the tweeter actually using the product. Our methods range from linear classifiers (e.g., logistic regression), classical nonlinear models (e.g., nearest neighbors), recent deep neural networks (e.g., convolutional neural networks), and ensembles of all these models in using different supervised training regimens (e.g., co-training). The focus is more on task specific system building than on building specific individual models. Overall, we demonstrate that it is possible to glean insights from social data on health related topics through natural language processing and machine learning with use-cases from substance use and mental health

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

SmokEng: Towards Fine-grained Classification of Tobacco-related Social Media Text

Contemporary datasets on tobacco consumption focus on one of two topics, either public health mentions and disease surveillance, or sentiment analysis on topical tobacco products and services. However, two primary considerations are not accounted for, the language of the demographic affected and a combination of the topics mentioned above in a fine-grained classification mechanism. In this paper, we create a dataset of 3144 tweets, which are selected based on the presence of colloquial slang related to smoking and analyze it based on the semantics of the tweet. Each class is created and annotated based on the content of the tweets such that further hierarchical methods can be easily applied. Further, we prove the efficacy of standard text classification methods on this dataset, by designing experiments which do both binary as well as multi-class classification. Our experiments tackle the identification of either a specific topic (such as tobacco product promotion), a general mention (cigarettes and related products) or a more fine-grained classification. This methodology paves the way for further analysis, such as understanding sentiment or style, which makes this dataset a vital contribution to both disease surveillance and tobacco use research.Comment: Accepted at the Workshop on Noisy User-generated Text (W-NUT) at EMNLP-IJCNLP 201

Analysis of Tweets for Social Media Health Applications

abstract: Social networking sites like Twitter have provided people a platform to connect with each other, to discuss and share information and news or to entertain themselves. As the number of users continues to grow there has been explosive growth in the data generated by these users. Such a vast data source has provided researchers a way to study and monitor public health. Accurately analyzing tweets is a difficult task mainly because of their short length, the inventive spellings and creative language expressions. Instead of focusing at the topic level, identifying tweets that have personal health experience mentions would be more helpful to researchers, governments and other organizations. Another important limitation in the current systems for social media health applications is the use of a disease-specific model and dataset to study a particular disease. Identifying adverse drug reactions is an important part of the drug development process. Detecting and extracting adverse drug mentions in tweets can supplement the list of adverse drug reactions that result from the drug trials and can help in the improvement of the drugs. This thesis aims to address these two challenges and proposes three systems. A generalizable system to identify personal health experience mentions across different disease domains, a system for automatic classifications of adverse effects mentions in tweets and a system to extract adverse drug mentions from tweets. The proposed systems use the transfer learning from language models to achieve notable scores on Social Media Mining for Health Applications(SMM4H) 2019 (Weissenbacher et al. 2019) shared tasks.Dissertation/ThesisMasters Thesis Computer Science 201