Predicting the Popularity of TED Talks

In this paper, we explore how to predict a TED talk’s popularity by its inherent features via machine learning techniques. we quantify a TED talk’s popularity as logarithmic transformation of its daily views and daily comments and include 43 features as predictors. We find that the ordinary least squares regression, ridge regression, and LASSO regression models perform well, and predictors such as a talk’s number of language translations, average Internet development environment when published, duration, main speaker’s occupation, as well as the timing it being uploaded have essential effects on its popularity. In the end, we also provide our suggestion on how to improve TED talks’ popularity within and beyond the scope of machine learning. All the materials in this research (including but not limited to original and processed data, figures, tables, etc.) are under the Creative Commons License: BY-NC-ND 3.0.Master of Science in Information Scienc

Linguistic Threat Assessment: Understanding Targeted Violence through Computational Linguistics

Language alluding to possible violence is widespread online, and security professionals are increasingly faced with the issue of understanding and mitigating this phenomenon. The volume of extremist and violent online data presents a workload that is unmanageable for traditional, manual threat assessment. Computational linguistics may be of particular relevance to understanding threats of grievance-fuelled targeted violence on a large scale. This thesis seeks to advance knowledge on the possibilities and pitfalls of threat assessment through automated linguistic analysis. Based on in-depth interviews with expert threat assessment practitioners, three areas of language are identified which can be leveraged for automation of threat assessment, namely, linguistic content, style, and trajectories. Implementations of each area are demonstrated in three subsequent quantitative chapters. First, linguistic content is utilised to develop the Grievance Dictionary, a psycholinguistic dictionary aimed at measuring concepts related to grievance-fuelled violence in text. Thereafter, linguistic content is supplemented with measures of linguistic style in order to examine the feasibility of author profiling (determining gender, age, and personality) in abusive texts. Lastly, linguistic trajectories are measured over time in order to assess the effect of an external event on an extremist movement. Collectively, the chapters in this thesis demonstrate that linguistic automation of threat assessment is indeed possible. The concluding chapter describes the limitations of the proposed approaches and illustrates where future potential lies to improve automated linguistic threat assessment. Ideally, developers of computational implementations for threat assessment strive for explainability and transparency. Furthermore, it is argued that computational linguistics holds particular promise for large-scale measurement of grievance-fuelled language, but is perhaps less suited to prediction of actual violent behaviour. Lastly, researchers and practitioners involved in threat assessment are urged to collaboratively and critically evaluate novel computational tools which may emerge in the future

Exploring Algorithmic Literacy for College Students: An Educator’s Roadmap

Research shows that college students are largely unaware of the impact of algorithms on their everyday lives. Also, most university students are not being taught about algorithms as part of the regular curriculum. This exploratory, qualitative study aimed to explore subject-matter experts’ insights and perceptions of the knowledge components, coping behaviors, and pedagogical considerations to aid faculty in teaching algorithmic literacy to college students. Eleven individual, semi-structured interviews and one focus group were conducted with scholars and teachers of critical algorithm studies and related fields. Findings suggested three sets of knowledge components that would contribute to students’ algorithmic literacy: general characteristics and distinguishing traits of algorithms, key domains in everyday life using algorithms (including the potential benefits and risks), and ethical considerations for the use and application of algorithms. Findings also suggested five behaviors that students could use to help them better cope with algorithmic systems and nine teaching strategies to help improve students’ algorithmic literacy. Suggestions also surfaced for alternative forms of assessment, potential placement in the curriculum, and how to distinguish between basic algorithmic awareness compared to algorithmic literacy. Recommendations for expanding on the current Association of College and Research Libraries’ Framework for Information Literacy for Higher Education (2016) to more explicitly include algorithmic literacy were presented

Attention Restraint, Working Memory Capacity, and Mind Wandering: Do Emotional Valence or Intentionality Matter?

Attention restraint appears to mediate the relationship between working memory capacity (WMC) and mind wandering (Kane et al., 2016). Prior work has identifed two dimensions of mind wandering—emotional valence and intentionality. However, less is known about how WMC and attention restraint correlate with these dimensions. Te current study examined the relationship between WMC, attention restraint, and mind wandering by emotional valence and intentionality. A confrmatory factor analysis demonstrated that WMC and attention restraint were strongly correlated, but only attention restraint was related to overall mind wandering, consistent with prior fndings. However, when examining the emotional valence of mind wandering, attention restraint and WMC were related to negatively and positively valenced, but not neutral, mind wandering. Attention restraint was also related to intentional but not unintentional mind wandering. Tese results suggest that WMC and attention restraint predict some, but not all, types of mind wandering

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Cognition, Language and Aging

Age-related changes in cognitive and language functions have been extensively researched over the past half-century. The older adult represents a unique population for studying cognition and language because of the many challenges that are presented with investigating this population, including individual differences in education, life experiences, health issues, social identity, as well as gender. The purpose of this book is to provide an advanced text that considers these unique challenges and assembles in one source current information regarding (a) language in the aging population and (b) current theories accounting for age-related changes in language function. A thoughtful and comprehensive review of current research spanning different disciplines that study aging will achieve this purpose. Such disciplines include linguistics, psychology, sociolinguistics, neurosciences, cognitive sciences, and communication sciences