Virality in the Digital Age: Contextualization, Messaging Strategies, and Framing Detection

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Description
Social networking platforms have redefined communication, serving as conduits forswift global information dissemination on contemporary topics and trends. This research probes information cascade (IC) dynamics, focusing on viral IC, where user-shared information gains rapid, widespread attention. Implications of IC span advertising, persuasion, opinion-shaping,

Social networking platforms have redefined communication, serving as conduits forswift global information dissemination on contemporary topics and trends. This research probes information cascade (IC) dynamics, focusing on viral IC, where user-shared information gains rapid, widespread attention. Implications of IC span advertising, persuasion, opinion-shaping, and crisis response. First, this dissertation aims to unravel the context behind viral content, particularly in the realm of the digital world, introducing a semi-supervised taxonomy induction framework (STIF). STIF employs state-of-the-art term representation, topical phrase detection, and clustering to organize terms into a two-level topic taxonomy. Social scientists then assess the topic clusters for coherence and completeness. STIF proves effective, significantly reducing human coding efforts (up to 74%) while accurately inducing taxonomies and term-to-topic mappings due to the high purity of its topics. Second, to profile the drivers of virality, this study investigates messaging strategies influencing message virality. Three content-based hypotheses are formulated and tested, demonstrating that incorporation of “negativity bias,” “causal arguments,” and “threats to personal or societal core values” - singularly and jointly - significantly enhances message virality on social media, quantified by retweet counts. Furthermore, the study highlights framing narratives’ pivotal role in shaping discourse, particularly in adversarial campaigns. An innovative pipeline for automatic framing detection is introduced, and tested on a collection of texts on the Russia-Ukraine conflict. Integrating representation learning, overlapping graph-clustering, and a unique Topic Actor Graph (TAG) synthesis method, the study achieves remarkable framing detection accuracy. The developed scoring mechanism maps sentences to automatically detect framing signatures. This pipeline attains an impressive F1 score of 92% and a 95% weighted accuracy for framing detection on a real-world dataset. In essence, this dissertation focuses on the multidimensional exploration of information cascade, uncovering the context and drivers of content virality, and automating framing detection. Through innovative methodologies like STIF, messaging strategy analysis, and TAG Frames, the research contributes valuable insights into the mechanics of viral content spread and framing nuances within the digital landscape, enriching fields such as advertisement, communication, public discourse, and crisis response strategies.
Date Created
2023
Agent

Mild Traumatic Brain Injury Executive Function Rehabilitation Through Serious Gamification

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Description
The purpose of the present study is to explore a potential rehabilitation alternative/additive, when time, insurance, finances, or lack of knowledge are limitations for mild traumatic brain injury (mTBI) executive function (EF) rehabilitation. The experimental intervention involved two sets of

The purpose of the present study is to explore a potential rehabilitation alternative/additive, when time, insurance, finances, or lack of knowledge are limitations for mild traumatic brain injury (mTBI) executive function (EF) rehabilitation. The experimental intervention involved two sets of participants an experimental group and a control group. Participants within the experimental and control groups partook in initial (week 1) and final (week 6) EF and TBI assessments. The experimental group additionally participated in four weeks (weeks 2 - 5) of an experimental intervention in beta stage of a web-based application. The aim of the intervention was to train EF skills planning, organization, and cognitive flexibility through serious gamification. At the conclusion of the study, it was observed that participants within the experimental group achieved higher scores on the experimental executive function assessment when compared to the control group. The difference in scores can be attributed to the weekly participation in executive function training.
Date Created
2023
Agent

Oasis – A Mobile Solution for Improving Gambling Self-Regulation

Description

The Oasis app is a self-appraisal tool for potential or current problem gamblers to take control of their habits by providing periodic check-in notifications during a gambling session and allowing users to see their progress over time. Oasis is backed

The Oasis app is a self-appraisal tool for potential or current problem gamblers to take control of their habits by providing periodic check-in notifications during a gambling session and allowing users to see their progress over time. Oasis is backed by substantial background research surrounding addiction intervention methods, especially in the field of self-appraisal messaging, and applies this messaging in a familiar mobile notification form that can effectively change user’s behavior. User feedback was collected and used to improve the app, and the results show a promising tool that could help those who need it in the future.

Date Created
2023-05
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An Analysis of Accessible Design in the Game Industry

Description

Games have traditionally had a high barrier to entry because they necessitate unique input devices, fast reaction times, high motor skills, and more. There has recently been a push to change the design process of these games to include people

Games have traditionally had a high barrier to entry because they necessitate unique input devices, fast reaction times, high motor skills, and more. There has recently been a push to change the design process of these games to include people with disabilities so they can interact with the medium of games as well. This thesis examines the current guiding principles of accessible design, who they are being developed by, and how they might help guide future accessible design and development. Additionally, it will look at modern games with accessibility features and classify them in terms of the Game Accessibility Guidelines. Then, using an interview with a lead developer at a game studio as aid, there will be an examination into modern game industry practices and what might be holding developers or studios back when it comes to accessible design. Finally, further suggestions for these developers and studios will be made in order to help them and others improve in making their games more accessible to people with disabilities.

Date Created
2023-05
Agent

Sensing Door States using Consumer Grade Barometric Pressure Sensor

Description

The aging population has become a pressing social issue, as the younger generation is busy with work and financial stability, leaving little time to care for the elderly. Technological advances in smart homes provide an opportunity for the elderly to

The aging population has become a pressing social issue, as the younger generation is busy with work and financial stability, leaving little time to care for the elderly. Technological advances in smart homes provide an opportunity for the elderly to live more comfortably and conveniently in their own homes. In this study, we conducted research on the definition of a smart home, the existing usage of the pressure sensor, the classification of the pressure sensor, and its working theory. We are curious about if a consumer-grade barometric sensor is sensitive enough in the home environment. Then, we set up the testing equipment with a consumer-grade barometric pressure sensor, an Adalogger FeatherWing, and an Arduino board. After programming the Arduino, we collected the data from the BME680 sensor in different states (open or closed) of the door, and then analyzed and visualized it using MATLAB. Furthermore, we also explored some potential scenarios and applications for the BME680 sensor. With the help of the BME680 sensor, smart home technology has the potential to improve the lives of older adults and ease the burden on younger generations.

Date Created
2023-05
Agent

"Can I Consider You My Friend?" Moving Beyond One-Sided Conversation in Social Robotics

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Description
As people begin to live longer and the population shifts to having more olderadults on Earth than young children, radical solutions will be needed to ease the burden on society. It will be essential to develop technology that can age with

As people begin to live longer and the population shifts to having more olderadults on Earth than young children, radical solutions will be needed to ease the burden on society. It will be essential to develop technology that can age with the individual. One solution is to keep older adults in their homes longer through smart home and smart living technology, allowing them to age in place. People have many choices when choosing where to age in place, including their own homes, assisted living facilities, nursing homes, or family members. No matter where people choose to age, they may face isolation and financial hardships. It is crucial to keep finances in mind when developing Smart Home technology. Smart home technologies seek to allow individuals to stay inside their homes for as long as possible, yet little work looks at how we can use technology in different life stages. Robots are poised to impact society and ease burns at home and in the workforce. Special attention has been given to social robots to ease isolation. As social robots become accepted into society, researchers need to understand how these robots should mimic natural conversation. My work attempts to answer this question within social robotics by investigating how to make conversational robots natural and reciprocal. I investigated this through a 2x2 Wizard of Oz between-subjects user study. The study lasted four months, testing four different levels of interactivity with the robot. None of the levels were significantly different from the others, an unexpected result. I then investigated the robot’s personality, the participant’s trust, and the participant’s acceptance of the robot and how that influenced the study.
Date Created
2022
Agent

Monocular Visual Odometry: Deep Learning vs Classical Approaches

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Description
Visual Odometry is one of the key aspects of robotic localization and mapping. Visual Odometry consists of many geometric-based approaches that convert visual data (images) into pose estimates of where the robot is in space. The classical geometric methods have

Visual Odometry is one of the key aspects of robotic localization and mapping. Visual Odometry consists of many geometric-based approaches that convert visual data (images) into pose estimates of where the robot is in space. The classical geometric methods have shown promising results; they are carefully crafted and built explicitly for these tasks. However, such geometric methods require extreme fine-tuning and extensive prior knowledge to set up these systems for different scenarios. Classical Geometric approaches also require significant post-processing and optimization to minimize the error between the estimated pose and the global truth. In this body of work, the deep learning model was formed by combining SuperPoint and SuperGlue. The resulting model does not require any prior fine-tuning. It has been trained to enable both outdoor and indoor settings. The proposed deep learning model is applied to the Karlsruhe Institute of Technology and Toyota Technological Institute dataset along with other classical geometric visual odometry models. The proposed deep learning model has not been trained on the Karlsruhe Institute of Technology and Toyota Technological Institute dataset. It is only during experimentation that the deep learning model is first introduced to the Karlsruhe Institute of Technology and Toyota Technological Institute dataset. Using the monocular grayscale images from the visual odometer files of the Karlsruhe Institute of Technology and Toyota Technological Institute dataset, through the experiment to test the viability of the models for different sequences. The experiment has been performed on eight different sequences and has obtained the Absolute Trajectory Error and the time taken for each sequence to finish the computation. From the obtained results, there are inferences drawn from the classical and deep learning approaches.
Date Created
2022
Agent

Classification of Fabric Based Soft Actuators and Feedback Controller for At-home Hand Rehabilitation

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Description
With an aging population, the number of later in life health related incidents like stroke stand to become more prevalent. Unfortunately, the majority those who are most at risk for debilitating heath episodes are either uninsured or under insured when

With an aging population, the number of later in life health related incidents like stroke stand to become more prevalent. Unfortunately, the majority those who are most at risk for debilitating heath episodes are either uninsured or under insured when it comes to long term physical/occupational therapy. As insurance companies lower coverage and/or raise prices of plans with sufficient coverage, it can be expected that the proportion of uninsured/under insured to fully insured people will rise. To address this, lower cost alternative methods of treatment must be developed so people can obtain the treated required for a sufficient recovery. The presented robotic glove employs low cost fabric soft pneumatic actuators which use a closed loop feedback controller based on readings from embedded soft sensors. This provides the device with proprioceptive abilities for the dynamic control of each independent actuator. Force and fatigue tests were performed to determine the viability of the actuator design. A Box and Block test along with a motion capture study was completed to study the performance of the device. This paper presents the design and classification of a soft robotic glove with a feedback controller as a at-home stroke rehabilitation device.
Date Created
2022
Agent

Low-Intensity Blood Flow Restriction Training as a Preoperative Rehabilitative Modality to Improve Postoperative Outcomes for Anterior Cruciate Ligament Reconstruction

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Description
One of the long-standing issues that has arisen in the sports medicine field is identifying the ideal methodology to optimize recovery following anterior cruciate ligament reconstruction (ACLR). The perioperative period for ACLR is notoriously heterogeneous in nature as it consists

One of the long-standing issues that has arisen in the sports medicine field is identifying the ideal methodology to optimize recovery following anterior cruciate ligament reconstruction (ACLR). The perioperative period for ACLR is notoriously heterogeneous in nature as it consists of many variables that can impact surgical outcomes. While there has been extensive literature published regarding the efficacy of various recovery and rehabilitation topics, it has been widely acknowledged that certain modalities within the field of ACLR rehabilitation need further high-quality evidence to support their use in clinical practice, such as blood flow restriction (BFR) training. BFR training involves the application of a tourniquet-like cuff to the proximal aspect of a limb prior to exercise; the cuff is inflated so that it occludes venous flow but allows arterial inflow. BFR is usually combined with low-intensity (LI) resistance training, with resistance as low as 20% of one-repetition maximum (1RM). LI-BFR has been used as an emerging clinical modality to combat postoperative atrophy of the quadriceps muscles for those who have undergone ACLR, as these individuals cannot safely tolerate high muscular tension exercise after surgery. Impairments of the quadriceps are the major cause of poor functional status of patients following an otherwise successful ACLR procedure; however, these impairments can be mitigated with preoperative rehabilitation done before surgery. It was hypothesized that the use of a preoperative LI-BFR training protocol could help improve postoperative outcomes following ACLR; primarily, strength and hypertrophy of the quadriceps. When compared with a SHAM control group, subjects who were randomized to a BFR intervention group made greater preoperative strength gains in the quadriceps and recovered quadriceps mass at an earlier timepoint than that of the SHAM group aftersurgery; however, the gains made in strength were not able to be maintained in the 8-week postoperative period. While these results do not support the use of LI-BFR from the short-term perspective after ACLR, follow-up data will be used to investigate trends in re-injury and return to sport rates to evaluate the efficacy of the use of LI-BFR from a long-term perspective.
Date Created
2022
Agent

Analysis of Machine Learning Assisted Fatigue Identification in Radiology Readings

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Description
Fatigue in radiology is a readily studied area. Machine learning concepts appliedto the identification of fatigue are also readily available. However, the intersection between the two areas is not a relative commonality. This study looks to explore the intersection of fatigue in

Fatigue in radiology is a readily studied area. Machine learning concepts appliedto the identification of fatigue are also readily available. However, the intersection between the two areas is not a relative commonality. This study looks to explore the intersection of fatigue in radiology and machine learning concepts by analyzing temporal trends in multivariate time series data. A novel methodological approach using support vector machines to observe temporal trends in time-based aggregations of time series data is proposed. The data used in the study is captured in a real-world, unconstrained radiology setting where gaze and facial metrics are captured from radiologists performing live image reviews. The captured data is formatted into classes whose labels represent a window of time during the radiologist’s review. Using the labeled classes, the decision function and accuracy of trained, linear support vector machine models are evaluated to produce a visualization of temporal trends and critical inflection points as well as the contribution of individual features. Consequently, the study finds valid potential justification in the methods suggested. The study offers a prospective use of maximummargin classification to demarcate the manipulation of an abstract phenomenon such as fatigue on temporal data. Potential applications are envisioned that could improve the workload distribution of the medical act.
Date Created
2022
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