Transcription task Figure 1: Three participants taking part in the study: (a) completing the personality questionnaire, (b) performing the composition task, and (c) performing the transcription task.

This study examines how user perceptions of information quality in social media posts vary across platforms, specifically Facebook, Twitter/X, and LinkedIn. An online survey was conducted to assess perceived reliability, accuracy, shareability, and fake news detection. Results showed no significant differences in perceived reliability, accuracy, or shareability across platforms, indicating users generally trust social media posts regardless of the platform. However, participants demonstrated low fake news detection accuracy, highlighting concerns about misinformation and platform accountability. Correlational analysis revealed that higher social media usage was associated with greater trust in posts but lower ability to detect fake news. Regression analysis further revealed that frequent users and individuals with higher education not only exhibited greater trust in and willingness to share posts but also demonstrated a lower ability to identify fake news.

The extensive use of computers for text entry has been linked to increased stress, depression, and sleep disturbances, adversely affecting performance. Recent trends involve using scent diffusers to counter these effects. However, the impact of scents on text entry performance is not well-studied. Our empirical study investigated the effects of self-selected pleasant scents on text composition and transcription performance. Results showed that while composing, users were slower with a scent present, potentially due to heightened focus on text quality. Scent did not alter accuracy or text length. In transcription tasks, although scent did not alter typing speed, it adversely affected accuracy, likely due to its impact on concentration levels. Despite these mixed results, users felt more effective and enjoyed the scent, indicating a preference for its continued use. This study opens avenues for further research into scents’ influence on computer-based tasks, potentially contributing to the evolving field of olfactory displays.

Silent speech is unaffected by ambient noise, increases accessibility, and enhances privacy and security. Yet current silent speech recognizers operate in a phrase-in/phrase-out manner, thus are slow, error prone, and impractical for mobile devices. We present MELDER, a Mobile Lip Reader that operates in real-time by splitting the input video into smaller temporal segments to process them individually. An experiment revealed that this substantially improves computation time, making it suitable for mobile devices. We further optimize the model for everyday use by exploiting the knowledge from a high-resource vocabulary using a transfer learning model. We then compare MELDER in both stationary and mobile settings with two state-of-the-art silent speech recognizers, where MELDER demonstrated superior overall performance. Finally, we compare two visual feedback methods of MELDER with the visual feedback method of Google Assistant. The outcomes shed light on how these proposed feedback methods influence users’ perceptions of the model’s performance.

Mobile text entry is difficult for people with motor impairments due to limited access to smartphones and the need for precise target selection on touchscreens. Text entry on smartwatches, on the other hand, has not been well explored for the population. Crownboard enables people with limited dexterity enter text on a smartwatch using its crown. It uses an alphabetical layout divided into eight zones around the bezel. The zones are scanned either automatically or manually by rotating the crown, then selected by pressing the crown. Crownboard decodes zone sequences into words and displays word suggestions. We validated its design in multiple studies. First, a comparison between manual and automated scanning revealed that manual scanning is faster and more accurate. Second, a comparison between clockwise and shortest-path scanning identified the former to be faster and more accurate. In the final study with representative users, only 30% participants could use the default Qwerty. They were 9% and 23% faster with manual and automated Crownboard, respectively. All participants were able to use both variants of Crownboard.

Free-hand mid-air Qwerty enables entering text in virtual reality without the use of controllers. However, it is much slower and more error prone than its physical counterpart, primarily due to the absence of haptic feedback and reduced spatial awareness. In this paper, we design three different ultrasonic haptic feedback for mid-air Qwerty: feedback only on keypress, on both touch and keypress, and gradual feedback that increases intensity as users push down a key. In a pilot study, the touch & press feedback performed significantly better both quantitatively and qualitatively. We then compare a mid-air Qwerty with and without touch & press feedback in a user study. Results revealed that haptic feedback improves entry speed by 16% and reduces error rate by 26%. Besides, most participants feel that it improves presence and spatial awareness in the virtual-world by maintaining a higher consistency with the real-world, and significantly reduces mental demand, effort, and frustration.

Selecting small targets is difficult on tiny displays due to the “fat-finger problem”. In this paper, we explore the possibility of using a force-based approach to target selection on smartwatches. First, we identify the most comfortable range of force on smartwatches. We then conduct a 1D Fitts’ law study to compare the performance of tap and force-tap. Results revealed that force-tap is significantly better in selecting smaller targets, while tap outperforms force-tap for bigger targets. We then developed two new force-based keyboards to demonstrate the feasibility of force input in practical scenarios. These single-row alphabetical keyboards enable character-level text entry by performing slides and variating contact force. In a user study, these keyboards yielded about 4 wpm with about 2% error rate, demonstrating the viability of force input on smaller screens.

GeShort is a novel method for one-handed text editing and formatting on mobile devices. It uses simple rules to facilitate direct cursor positioning, gestural shortcuts inspired by keyboard hotkeys for editing and formatting, and a floating clipboard to enable delayed, repeated, and block editing. A comparison between GeShort and the default Google keyboard revealed that users perform editing and formatting tasks about 11% and 22% faster, respectively, with GeShort. This is achieved by significantly reducing selection time by 11% and action time by 17%. A second study comparing the clipboard features of the two methods revealed that users perform advanced editing tasks 34% faster with GeShort. Besides, participants find GeShort less onerous in mental demand, physical demand, and effort, which likely contribute to the overall performance gain. They also perceive GeShort as faster and easier to use, feel that its functions are better integrated, thus want to keep using it on their devices.

This work compares four mid-air target selection methods (Push, Tap, Dwell, Pinch) with two types of ultrasonic haptic feedback (Select, HoverSelect) in a Fitts’ law experiment. Results revealed that Tap is the fastest, the most accurate, and one of the least physically and cognitively demanding selection methods. Pinch is relatively fast but error prone and physically and cognitively demanding. Dwell is slowest by design, yet the most accurate and the least physically and cognitively demanding. Both haptic feedback methods improve selection performance by increasing users’ spatial awareness. Particularly, Push augmented with Hover & Select feedback is comparable to Tap. Besides, participants perceive the selection methods as faster, more accurate, and more physically and cognitively comfortable with the haptic feedback methods.

We investigate silent speech as a hands-free selection method in eye-gaze pointing. We first propose a stripped-down image-based model that can recognize a small number of silent commands almost as fast as state-of-the-art speech recognition models. We then compare it with other hands-free selection methods (dwell, speech) in a Fitts' law study. Results revealed that speech and silent speech are comparable in throughput and selection time, but the latter is significantly more accurate than the other methods. A follow-up study revealed that target selection around the center of a display is significantly faster and more accurate, while around the top corners and the bottom are slower and error prone. We then present a method for selecting menu items with eye-gaze and silent speech. A study revealed that it significantly reduces task completion time and error rate.

Mobile clients for telepresence robots are cluttered with interactive elements that either leave a little room for the camera feeds or occlude them. Many do not provide meaningful feedback on the robot's state and most require the use of both hands. These make maneuvering telepresence robots difficult with mobile devices. TiltWalker enables controlling a telepresence robot with one hand using tilt gestures with a smartphone. In a series of studies, we first justify the use of a Web platform, determine how far and fast users can tilt without compromising the comfort and the legibility of the display content, and identify a velocity-based function well-suited for control-display mapping. We refine TiltWalker based on the findings of these studies, then compare it with a default method in the final study. Results revealed that TiltWalker is significantly faster and more accurate than the default method. Besides, participants preferred TiltWalker's interaction methods and graphical feedback significantly more than those of the default method.

Speech recognition is unreliable in noisy places, compromises privacy and security when around strangers, and inaccessible to people with speech disorders. Lip reading can mitigate many of these challenges but the existing silent speech recognizers for lip reading are error prone. Developing new recognizers and acquiring new datasets is impractical for many since it requires enormous amount of time, effort, and other resources. To address these, first, we develop LipType, an optimized version of LipNet for improved speed and accuracy. We then develop an independent repair model that processes video input for poor lighting conditions, when applicable, and corrects potential errors in output for increased accuracy. We then test this model with LipType and other speech and silent speech recognizers to demonstrate its effectiveness.

Silent speech input converts non-acoustic features like tongue and lip movements into text. It has been demonstrated as a promising input method on mobile devices and has been explored for a variety of audiences and contexts where the acoustic signal is unavailable (e.g., people with speech disorders) or unreliable (e.g., noisy environment). Though the method shows promise, very little is known about peoples’ perceptions regarding using it. In this work, first, we conduct two user studies to explore users’ attitudes towards the method with a particular focus on social acceptance and error tolerance. Results show that people perceive silent speech as more socially acceptable than speech input and are willing to tolerate more errors with it to uphold privacy and security. We then conduct a third study to identify a suitable method for providing real-time feedback on silent speech input. Results show users find an abstract feedback method effective and significantly more private and secure than a commonly used video feedback method.

Picking numbers is arguably the most frequently performed input task on smartwatches. This paper presents three new methods for picking numbers on smartwatches by performing directional swipes, twisting the wrist, and varying contact force on the screen. Unlike the default number picker, the proposed methods enable users to actively switch between slow-and-steady and fast-and-continuous increments and decrements during the input process. We evaluated these methods in two user studies. The first compared the new methods with the default input stepper method in both stationary and mobile settings. The second compared them for individual numeric values and values embedded in text. In both studies, swipe yielded a significantly faster input rate. Participants also found the method faster, more accurate, and the least mentally and physically demanding compared to the other methods. Accuracy rates were comparable between the methods.

Senorita is a novel two-thumb virtual chorded keyboard for mobile devices. It arranges the letters on eight keys in a single row by the bottom edge of the device based on letter frequencies and the anatomy of the thumbs. Unlike most chorded methods, it provides visual cues to perform the chording actions in sequence, instead of simultaneously, when the actions are unknown, facilitating "learning by doing". Its compact design leaves most of the screen available and its position near the edge accommodates eyes-free text entry. In a longitudinal study with a smartphone, Senorita yielded on average 14 wpm. In a short-term study with a tablet, it yielded on average 9.3 wpm. In the final longitudinal study, it yielded 3.7 wpm with blind users, surpassing their Qwerty performance. Low vision users yielded 5.8 wpm. Further, almost all users found Senorita effective, easy to learn, and wanted to keep use it.

We propose a method for accommodating predictive number entry and editing on mobile devices via the suggestion bar of a virtual keyboard. We developed a simple predictive system to demonstrate the benefit of this method. It utilizes text-based querying and regular expression to suggest the most probable next numeric actions in the suggestion bar along with word suggestions. We evaluated this method in two user studies. The first explored number entry and the second explored editing. Results revealed that the proposed method significantly increases number entry and editing speed and accuracy. It reduces the number of actions needed per task. It also significantly reduces the time and effort needed to fix errors. Subjective analysis revealed that almost all participants found the method faster, more reliable, and easier to use than the conventional method, thus wanted to keep using it on their mobile devices.

This paper presents Woodpecker, a playful mobile user authentication method that enables users to authenticate themselves by performing back-of-device tap rhythms. It uses the microphone and accelerometer data of an off-the-shelf smart-phone to compare the sequence, frequency, and intensity of tap rhythms to authenticate users. In a study, Woodpecker yielded a moderate accuracy (70%) and a low successful attack rate (17%) in an ideal shoulder surfing threat model with only three sample rhythms. Besides, most participants found the method easy-to-use and more secure than the conventional methods, thus wanted to keep using it on their devices.

This work augments novel text translation features to the suggestion bar of a virtual keyboard to facilitate fast and easy translation on mobile devices. The method was evaluated in two user studies. In the first study, native Hindi and Mandarin speakers exchanged text messages in each other's language using the method. All participants found the method fast and easy, the quality and the flow of the conversation satisfactory, and wanted to use it frequently for multilingual and polyglot texting. In the second study, participants performed various translation tasks using the proposed method and the default Google keyboard's translation feature. Results revealed that the proposed method was significantly faster and required fewer actions for translation tasks. Further, most participants found the method more effective and user-friendly.

Touch is the predominant method of text entry on mobile devices. However, these devices also facilitate tilt-based input using accelerometers and gyroscopes. This paper presents the design and evaluation of TiltWriter, a non-touch, tilt-based text entry technique. TiltWriter aims to supplement conventional techniques when precise touch is not convenient or possible (e.g., the user lacks sufficient motor skills). Two keyboard layouts were designed and evaluated: Qwerty, and "Custom", a layout inspired by the telephone keypad. Novice participants in a longitudinal study achieved speeds of 12.1 wpm for Qwerty, 10.7 wpm for Custom. Error rate averaged 0.76% for Qwerty, 0.62% for Custom. A post-study extended session yielded 15.2 wpm with Custom, versus 11.5 wpm with Qwerty. Results and participant feedback suggest that a selection dwell time between 700 - 800 ms benefits accuracy.

This work augments context-sensitive app prediction feature to the suggestion bar of a mobile virtual keyboard to accommodate fast and easy information acquisition and sharing in textual conversations. The purpose is to eliminate the need for switching between apps while typing. A user study revealed that the proposed method improves performance both in terms of speed and effort for common tasks, such as playing a song or finding and sharing the address of a restaurant. Post-study questionnaire revealed that all participants found the method fast, easy, and likely to facilitate more engaging and meaningful textual conversations. All wanted to keep using it on their mobile devices.

This paper presents results of a comparative study that investigated the effects of different types of keyboard backgrounds (themes) on actual and perceived text entry performance, in terms of speed and accuracy. Two color and two image backgrounds were compared with the default Google Android keyboard. Results revealed that keyboard background does not affect actual performance, however has a significant effect on perceived performance. Most participants felt that image backgrounds, regardless of whether they were pre or self-selected, affected their speed and accuracy. This suggests that it may be possible to enhance one's text entry experience simply by designing an effective keyboard theme. This paper concludes with reflections on how these findings could benefit text entry researchers and keyboard developers.

This paper reviews and categorizes the most common types of selfie sticks available in the market and discusses their design and usability. Through a theoretical analysis, it demonstrates how most commercial selfie sticks ignore important human factors, including ergonomics. It, then, presents results of an online survey, where selfie stick users (N=105), predominantly from the Republic of Korea, rated the usability of their selfie sticks. Results of the survey provided an insight into users' selfie stick usage behavior and suggested that most commercial selfie sticks are unergonomic, causing the users short-term fatigues that could "in theory" turn into chronic over the time with extensive use. Finally, based on the survey results, the paper makes recommendations for future design.

We present Paper Ladder, a low-cost, light-weight, and paper-based variant of the Sticky Ladder rating scale to collect children's product preferences in user studies. This paper version revamps the original, effective but arguably impractical evaluation method by making it more accessible to researchers. In this paper, we show its potential with preliminary results from a pilot study, where 45 grade-2 children used a Paper Ladder to rate their preference for different types of correctness feedback in a math app. Results suggest that Paper Ladder could be an effective and valuable instrument for conducting user studies with young children.

This paper presents results of a survey that explored the challenges low-income mobile users of Dhaka, Bangladesh (N = 131) face in mobile text entry. Results revealed that all users use the Bengali language at some capacity to compose text, yet many (are forced to) write with the Roman alphabet. Both feature phone and smartphone users feel that the existing text entry techniques are difficult to learn and use. The fact that some knowledge of the English language is necessary, even to use many popular Bengali text entry techniques, frustrates them as it compromises their entry speed and accuracy. Results also suggest that mobile phones and mobile text entry are more popular among younger and educated users. Further, smartphone users spend more time and engage in more text entry episodes than feature phone users.

This paper presents results of an exploratory study that examined the effects of different types of correctness feedback on children's actual and perceived performance with a math app. In the study, forty-flve grade-2 students solved easy, moderate, and hard drill questions with a math app augmented with textual, icon, and emoticon correctness feedback. Results suggested that, for the most part, neither the feedback type nor the difficulty level affect children's actual and perceived performance with the app.

We developed a custom tangible that uses LED arrays around the edges as a low-resolution display to provide real-time visual feedback on the current state of the system. We developed a guideline for mapping different types of edge feedback to different tangible interactions. We evaluated its effectiveness in an informal user study where users interacted with a tabletop and tangible system with the edge feedback enabled. Results suggest that edge feedback provides a better understanding of the system.

We present a new smart-restorable backspace technique to facilitate correction of "overlooked" errors on touchscreen-based tablets. We conducted an empirical study to compare the new backspace technique with the conventional one. Results of the study revealed that the new technique improves the overall text entry performance, both in terms of speed and operations per character, by significantly reducing error correction efforts. In addition, results showed that most users preferred the new technique to the one they use on their tablets, and found it easy to learn and use. Most of them also felt that it improved their overall text entry performance, thus wanted to keep using it.

WebTEM is a Web application to record text entry metrics. It is developed with common Web technologies, thus works on any device with a modern Web browser, and with any keyboard. We evaluated its effectiveness in an empirical study that compared the default Apple iOS, Google Android, and Microsoft Windows Phone keyboards. Results of the study highlighted mobile users' increased dependency on autocorrections.

We present a novel color-coded feedback method that highlights the types of corrections made by autocorrections. We then present results of a longitudinal user study with 7-8-year-old children that compared the new method with the conventional autocorrection feedback method, in terms of leaning new words and spelling. Results suggested that the new method better accommodates learning new words. Interestingly, learning was observed with the conventional feedback method as well, demanding further investigation into whether predictive methods are truly a barrier to learning new words and spelling.

Actibles are an open source hardware/software platform for creating active tangibles. Actibles contain a smartwatch core, which eases both hardware and software development, and enables application developers to leverage various web technologies. The smartwatch core is augmented by custom hardware that enables an expanded set of tangible interactions, including shaking, tilting, stacking and neighbouring, as well as on-screen gestures and integrated LED feedback. Actibles can be used both independently or in conjunction with other devices, such as interactive tabletops. We describe the Actible's technical specifications and demonstrate several example applications.

We present Active Pathways, an active tangible and tabletop system that aims to support collaborative discovery and learning in biochemical modeling. Our work extends ideas from embodied cognition that suggest that interactive systems that support model building by coupling actions made with the motor system to the dynamic properties of external and internal models can enhance the potential for insight and discoveries. We describe the motivation for our work and the design and development of our system. We also present the results of a user study with pairs of novice modelers, which suggest that our system is not only easy to use and learn but also successful at facilitating an understanding of complex systems and supporting collaboration.

We present Sparse Tangibles, a tabletop and active tangible-based framework to support cross-platform, collaborative gene network exploration using a Web interface. It uses smartwatches as active tangibles to allow query construction on- and off-the-table. We expand their interaction vocabulary using inertial sensors and a custom case. We also introduce a new metric for measuring the "confidence level" of protein and genetic interactions. Three expert biologists evaluated the system and found it fun, useful, easy to use, and ideal for collaborative explorations.

TEI researchers/designers are often discouraged from building complex interactive environments by the requirement of high technical knowledge. In this studio/workshop, we present the ROSS Toolkit that offers tools to abstract/automate low-level programming of technical details, thereby simplifying the design and programming of interactions between heterogeneous networked devices. Participants will be first introduced to the toolkit functionality to cope with different technical issues. In a second part, they will experiment with the toolkit by developing use cases of increasing complexity that will involve off-the-shelf devices, interactive surfaces, and custom-made tangible artifacts. We expect participants to learn what are the opportunities and challenges for the development of responsive and interactive environments.

This tutorial explores the possibility of using touchscreen-based mobile devices as active tangibles on an interactive tabletop surface. The tutorial starts with an open discussion about various aspects of tangible interaction, including an overview of different approaches and design principles. It then guides participants through the design and development of innovative interaction techniques, where mobile phones are used as active tangibles on a shared tabletop display. The intent is to encourage the mobile HCI community to further explore the possibility of using everyday devices such as mobile phones as tangibles.

We present two new user authentication techniques that look and feel like slide-to-unlock. The intention is to encourage users, who do not use a user authentication technique on their devices, to start using one. Results of a user study showed that the new techniques perform relatively well compared

In this article, we present a new user authentication technique for touchscreen-based smartphone users that augments pseudo touch pressure as an extra security measure to the conventional digit-lock technique. The new technique enhances security by offering more unique password combinations than the

In this paper, we present results of two user studies that compared the performance of touch-based and pen-based gesture input on capacitive touchscreens for both adult and 8-11 years old child users. Results showed that inputting gestures with pen was significantly faster and more accurate than touch for adult users. However, no significant effect of input method was observed on performance for child users. Similarly, user experience evaluation showed that a large number of adult users favoured one technique over the other and/or found a technique more comfortable to use than the other, while child users were mostly neutral. This trend, however, was not statistically significant.

This paper presents a new tap and gesture hybrid method for authenticating mobile device users. The new technique augments four simple gestures - up, down, left, and right, to the dominant digit lock technique, allowing users to either tap or perform any one of the four gestures on the digit keys. It offers in total 6250000 unique four-symbol password combinations, which is substantially more than the conventional techniques. Results of a pilot study showed that the new technique was slower and more error prone than the digit lock technique. However, we believe with practice it could get faster and more accurate. Also, most users were comfortable and all of them felt more secured while using the new technique.

Researchers have developed many models to predict and understand human performance in text entry. Most of the models are specific to a technology or fail to account for human factors and variations in system parameters, and the relationship between them. Moreover, the process of fixing errors and its effects on text entry performance has not been studied. Here, we first analyze real-life text entry error correction behaviors. We then use our findings to develop a new model to predict the cost of error correction for character-based text entry technologies. We validate our model against quantities derived from the literature, as well as with a user study. Our study shows that the predicted and observed cost of error correction correspond well. At the end, we discuss potential applications of our new model.