Filtering by
- All Subjects: Social Media
Despite the importance of personal information, in many cases people do not reveal this information to the public. Predicting the hidden or missing information is a common response to this challenge. In this thesis, we address the problem of predicting user attributes and future or missing links using an egocentric approach. The current research proposes novel concepts and approaches to better understand social media users in twofold including, a) their attributes, preferences, and interests, and b) their future or missing connections and interactions. More specifically, the contributions of this dissertation are (1) proposing a framework to study social media users through their attributes and link information, (2) proposing a scalable algorithm to predict user preferences; and (3) proposing a novel approach to predict attributes and links with limited information. The proposed algorithms use an egocentric approach to improve the state of the art algorithms in two directions. First by improving the prediction accuracy, and second, by increasing the scalability of the algorithms.
Adverse Drug Reaction (ADR) identification. Such methods employ a step of drug search followed by classification of the associated text as consisting an ADR or not. Although this method works efficiently for ADR classifications, if ADR evidence is present in users posts over time, drug mentions fail to capture such ADRs. It also fails to record additional user information which may provide an opportunity to perform an in-depth analysis for lifestyle habits and possible reasons for any medical problems.
Pre-market clinical trials for drugs generally do not include pregnant women, and so their effects on pregnancy outcomes are not discovered early. This thesis presents a thorough, alternative strategy for assessing the safety profiles of drugs during pregnancy by utilizing user timelines from social media. I explore the use of a variety of state-of-the-art social media mining techniques, including rule-based and machine learning techniques, to identify pregnant women, monitor their drug usage patterns, categorize their birth outcomes, and attempt to discover associations between drugs and bad birth outcomes.
The technique used models user timelines as longitudinal patient networks, which provide us with a variety of key information about pregnancy, drug usage, and post-
birth reactions. I evaluate the distinct parts of the pipeline separately, validating the usefulness of each step. The approach to use user timelines in this fashion has produced very encouraging results, and can be employed for a range of other important tasks where users/patients are required to be followed over time to derive population-based measures.
The purpose of this applied project was to research potential methods for conducting performance and evaluation observations on users of Positive Train Control (PTC) and recommend the most effective measures of performance (MOPs) and measures of efficiency (MOEs) of those users. I conducted a study to collect and analyze what data could be observed and examined most effectively to produce causal explanations of behaviors when utilizing the PTC system. This study was done through literature review, interviews of PTC users and trainers, and through direct observations as I rode on trains watching crews interact with the system. Additionally, I researched several studies on human computer interface (HCI) usability studies of various software applications. Based upon the results, I recommend that direct-participant observations be employed and apply both the system and individual MOPs and MOEs identified in the report to track user’s proficiency. The data collected from these observations can be centralized and used to identify behavioral trends, drive corrective actions, create future policies as well as training content. These observations will address the need to have structured observations which allow observers to focus undistracted on the specific behaviors that affect train operations. This database would also identify employees that may need additional or refresher training.
This report details the communication training workshop from inception to implementation. The overall goal of the workshop was to give the company's internal employees the tools necessary to effectively communicate with the organization's external employees. Developing the workshop required first determining the organization's key challenges. From there it was necessary to identify which of those challenges would be improved through improved communication. The observation method was used to research where communication between internal and external employees commonly broke down. Once the significant communication challenges were identified, the workshop was developed and implemented. This report examines the effectiveness of the workshop in detail and outlines both the successes and the challenges the workshop faced. There are detailed plans to improve the workshop as well as a thorough explanation as to why permanent implementation of Communication Training Workshop will be beneficial to the organization.
GetOverlanding was created to help overlanders successfully and safely explore the world, whether it be their immediate area or the far corners of the world through informative, educational, and engaging online content.
The usage of social media during social and political campaigns has been the subject of a lot of social science studies including the Occupy Wall Street movement, The Arab Spring, the United States (US) election, more recently The Brexit campaign. The wide
spread usage of social media in this space and the active participation of people in the discussions on social media made this communication channel a suitable place for spreading propaganda to alter public opinion.
An interesting feature of twitter is the feasibility of which bots can be programmed to operate on this platform. Social media bots are automated agents engineered to emulate the activity of a human being by tweeting some specific content, replying to users, magnifying certain topics by retweeting them. Network on these bots is called botnets and describing the collaboration of connected computers with programs that communicates across multiple devices to perform some task.
In this thesis, I will study how bots can influence the opinion, finding which parameters are playing a role in shrinking or coalescing the communities, and finally logically proving the effectiveness of each of the hypotheses.