Topic Detection and Tracking in Personal Search History

This thesis describes a system for tracking and detecting topics in personal search history. In particular, we developed a time tracking tool that helps users in analyzing their time and discovering their activity patterns. The system allows a user to specify interesting topics to monitor with a keyword description. The system would then keep track of the log and the time spent on each document and produce a time graph to show how much time has been spent on each topic to be monitored. The system can also detect new topics and potentially recommend relevant information about them to the user. This work has been integrated with the UCAIR Toolbar, a client side agent. Considering limited resources on the client side, we designed an e????cient incremental algorithm for topic tracking and detection. Various unsupervised learning approaches have been considered to improve the accuracy in categorizing the user log into appropriate categories. Experiments show that our tool is effective in categorizing the documents into existing categories and detecting the new useful catgeories. Moreover, the quality of categorization improves over time as more and more log is available

Discovering conversational topics and emotions associated with Demonetization tweets in India

Social media platforms contain great wealth of information which provides us opportunities explore hidden patterns or unknown correlations, and understand people's satisfaction with what they are discussing. As one showcase, in this paper, we summarize the data set of Twitter messages related to recent demonetization of all Rs. 500 and Rs. 1000 notes in India and explore insights from Twitter's data. Our proposed system automatically extracts the popular latent topics in conversations regarding demonetization discussed in Twitter via the Latent Dirichlet Allocation (LDA) based topic model and also identifies the correlated topics across different categories. Additionally, it also discovers people's opinions expressed through their tweets related to the event under consideration via the emotion analyzer. The system also employs an intuitive and informative visualization to show the uncovered insight. Furthermore, we use an evaluation measure, Normalized Mutual Information (NMI), to select the best LDA models. The obtained LDA results show that the tool can be effectively used to extract discussion topics and summarize them for further manual analysis.Comment: 6 pages, 11 figures. arXiv admin note: substantial text overlap with arXiv:1608.02519 by other authors; text overlap with arXiv:1705.08094 by other author

Automated Big Text Security Classification

In recent years, traditional cybersecurity safeguards have proven ineffective against insider threats. Famous cases of sensitive information leaks caused by insiders, including the WikiLeaks release of diplomatic cables and the Edward Snowden incident, have greatly harmed the U.S. government's relationship with other governments and with its own citizens. Data Leak Prevention (DLP) is a solution for detecting and preventing information leaks from within an organization's network. However, state-of-art DLP detection models are only able to detect very limited types of sensitive information, and research in the field has been hindered due to the lack of available sensitive texts. Many researchers have focused on document-based detection with artificially labeled "confidential documents" for which security labels are assigned to the entire document, when in reality only a portion of the document is sensitive. This type of whole-document based security labeling increases the chances of preventing authorized users from accessing non-sensitive information within sensitive documents. In this paper, we introduce Automated Classification Enabled by Security Similarity (ACESS), a new and innovative detection model that penetrates the complexity of big text security classification/detection. To analyze the ACESS system, we constructed a novel dataset, containing formerly classified paragraphs from diplomatic cables made public by the WikiLeaks organization. To our knowledge this paper is the first to analyze a dataset that contains actual formerly sensitive information annotated at paragraph granularity.Comment: Pre-print of Best Paper Award IEEE Intelligence and Security Informatics (ISI) 2016 Manuscrip