Shane E. Halse

Are We Becoming Super-Human Cyborgs? Examination of Technomorphism and the Creation of a Technomorphic Tendencies Scale

While traditionally researchers have focused on making robotics more user-friendly from a human perspective, a new theory has begun to take shape in which the human makes decisions based on how a robot would. The following study examines the concept of technomorphism which is the attribution of technological features and characteristics to humans. Because there is very little empirical or theoretical research performed in this area, researchers set out to formally define technomorphism as well as create a scale to measure a person’s propensity to use a technomorphic schema. The findings from this work should help fuel the desire of others in the field to think about the potential influences of technomorphism during the design and implementation of new devices as well as in how we technology may influence how we perceive each other.

An emotional step toward automated trust detection in crisis social media

ABSTRACT To this date, research on crisis informatics has focused on the detection of trust in Twitter data through the use of message structure, sentiment, propagation and author. Little research has examined the usefulness of these messages in the crisis response domain. In this paper, we characterize tweets, which are perceived useful or trustworthy, and determine their main features as one possible dimension to identify useful messages in case of crisis. In addition, we examine perceived emotions of these messages and how the different emotions affect the perceived usefulness and trustworthiness. Our analysis is carried out on two datasets gathered from Twitter concerning Hurricane Sandy in 2012 and the Boston Bombings in 2013. The results indicate that there is a high correlation between trustworthiness and usefulness, and, interestingly, that there is a significant difference in the perceived emotions that contribute to each of these. Our findings are poised to impact how messages from social media data are analyzed for use in crisis response.

Humans to Robots: How Technomorphic Features Shape our Perceptions of Each Other

It is becoming commonplace for humans to use technology to enhance and augment their understanding of the world. In this study, we investigated whether attention to these forms of technology can be predicted by scores on the Technomorphic Tendencies Scale (TTS) (Lum et al., 2011). Participants completed the TTS and were eye tracked while viewing pictures of models wearing various types of technological devices (e.g., an eye tracker; a Bluetooth headset). Higher TTS scorers tended to have more fixations and for a shorter duration of time when compared with the lower TTS Scorers. The Technomorphic Tendencies Scale is predictive of attention directed to technology when making first impressions.

So you Think you Know Football? Effects of Individual Differences on Video Game Performance

A plethora of research has examined video games in the context of training and violence. However, little has been done in examining the individual differences that may exist as it relates to success or failure during game play. Few studies have focused on empirically testing usability and performance issues specifically related to sports games. In this study, a football simulation video game was used to investigate how video game experience interacts with football knowledge in explaining performance within the game. Football video game simulations are a complicated game that appears best played when the user has both knowledge of football and experience playing video games. This study has implications for the individual differences that dictate performance within games.

Automated Emergence of a Crisis Situation Model in Crisis Response Based on Tweets

During a crisis, being able to understand quickly the situation on-site is crucial for the responders to take relevant decisions together. Social media, in particular Twitter, have proved to be a means for rapidly getting information from the field. However, the deluge of data is heterogeneous in many ways (location, trust, content, vocabulary, etc.), and getting a model of the crisis situation still requires laborious human actions. In addition, depending on which kind of information is mined from them, tweets have to be handle one-by-one (e.g. find victims), or as a whole - amount of tweets - (e.g. occurence of an event). This paper proposes a framework for automatically extracting, interpreting and aggregating streams of tweets to characterize crisis situations. It is based on a specific metamodel that determines the different concepts required to model a crisis situation.

Embracing human noise as resilience indicator: twitter as power grid correlate

Abstract There are typically two approaches for measuring disaster resilience: technically dynamic measures produced by sensors attached to physical objects and socially static metrics that engage demographic indicators within a given geographic location. Although these approaches allow resilience to be represented before and after disruption, it can be difficult to measure resilient behavior during an event. We propose that social media data can be used to nowcast the ongoing state of critical infrastructure during a disaster. Through an analysis of tweets made during Hurricane Sandy and power outage data obtained after the event, we find that tweets that mention power, utility, or electricity were correlated with loss of power. We conclude with a discussion of barriers to realizing this concept.

Improving the Utility of Social Media Data to Emergency Responders through Emotional Content Detection

In the following paper, we will present an alternate method for the detection of emotional content within social media data. Current research has presented the traditional bag-of-words method in which a predefined corpus is used to measure the emotional context of each word within a message. Here we present a method in which a small subset of the data is labeled to generate a corpus which is then used to detect emotional content within the data. This research is being conducted on the dataset from hurricane Sandy in 2012. Our findings show an improvement upon the bag-of-words method. These findings would further the current research in improving the utilization of social media data within crisis response. In doing this we allow the average citizen to provide beneficial data to those in decision making roles. KEywoRDS Emotion, Hurricane, Sandy, Sentiment, Trust, Twitter, Usefulness

Spatial perception orientation task (SPOT): developing an accessible tool for measuring spatial working memory.

Software applications are becoming increasingly realistic and complex which creates greater opportunities for work, gameplay and education. However, the limitations of human working memory create serious constraints for software and hardware designers and developers. Working memory capacity differs across individuals and shows growth through early childhood and decline in many older adults. However, recent research has shown that working memory is trainable and can improve problems such as attentional deficit disorder and cognitive problems in aging. The current work describes the development and preliminary validation of SPOT; the Spatial Perception Orientation Task. Spatial working memory is particularly important to spatial cognition, including mental rotation and spatial visualization. It is also important to understanding navigation in complex game worlds. After validation is complete, HCI practitioners will find SPOT easy to use, and available on-line. It will also be usable for different age groups and for those with color deficiencies.

Does This Robot Make Me Look Smart? How the Addition of a Robotic Pet Influences First Impressions

The current study examined whether the addition of either a robotic cat or a robotic dog to the picture of a male or female model, could affect the participants first impressions of the model. The participants were directed to a website and randomly assigned to a picture. Next, they answered a survey related to their perceptions of the model. Individual differences including participant gender, entity type, and response on robot attitudes scale were found. The results indicate that the items around or with us can be particularly important in public and social settings where first impressions can be the only ones a person gets to make.