Does emotion influence the use of auto-suggest during smartphone typing?

Typing based interfaces are common across many mobile applications, especially messaging apps. To reduce the difficulty of typing using keyboard applications on smartphones, smartwatches with restricted space, several techniques, such as auto-complete, auto-suggest, are implemented. Although helpful, these techniques do add more cognitive load on the user. Hence beyond the importance to improve the word recommendations, it is useful to understand the pattern of use of auto-suggestions during typing. Among several factors that may influence use of auto-suggest, the role of emotion has been mostly overlooked, often due to the difficulty of unobtrusively inferring emotion. With advances in affective computing, and ability to infer user's emotional states accurately, it is imperative to investigate how auto-suggest can be guided by emotion aware decisions. In this work, we investigate correlations between user emotion and usage of auto-suggest i.e. whether users prefer to use auto-suggest in specific emotion states. We developed an Android keyboard application, which records auto-suggest usage and collects emotion self-reports from users in a 3-week in-the-wild study. Analysis of the dataset reveals relationship between user reported emotion state and use of auto-suggest. We used the data to train personalized models for predicting use of auto-suggest in specific emotion state. The model can predict use of auto-suggest with an average accuracy (AUCROC) of 82% showing the feasibility of emotion-aware auto-suggestion

How do technologies meet the needs of the writer with dyslexia? An examination of functions scaffolding the transcription and proofreading in text production aimed towards researchers and practitioners in education

Technological reading and writing tools can help students with dyslexia improve their writing, but students do not use reading and writing functions as much as expected. However, research addressing relevant technological functions is scarce. This study explored the needs of writers with dyslexia and how technological writing tools developed for three Nordic languages meet these needs. Snowball sampling was used to identify different technological features, spellchecker, word prediction, auto-correction, text-to-speech and speech-to-text functions available in nine widely used programmes were investigated. The results indicated that students with moderate spelling difficulties can now achieve accurate spellings using the most sophisticated spelling aids; however, most of these features require time and attention, and this can disturb writing fluency and hamper text production. The implication of this study is that the underlying conflict between spelling accuracy and writing fluency must be actively managed, which necessitates competence in the use of technological tools for both students and teachers in school. Also, further development of tools for scaffolding transcription must consider the dilemma of achieving both writing fluency and spelling accuracy. Further, the accuracy of the aid for students with severe spelling difficulties remains unclear and must be investigated.publishedVersio

On the Social and Technical Challenges of Web Search Autosuggestion Moderation

Past research shows that users benefit from systems that support them in their writing and exploration tasks. The autosuggestion feature of Web search engines is an example of such a system: It helps users in formulating their queries by offering a list of suggestions as they type. Autosuggestions are typically generated by machine learning (ML) systems trained on a corpus of search logs and document representations. Such automated methods can become prone to issues that result in problematic suggestions that are biased, racist, sexist or in other ways inappropriate. While current search engines have become increasingly proficient at suppressing such problematic suggestions, there are still persistent issues that remain. In this paper, we reflect on past efforts and on why certain issues still linger by covering explored solutions along a prototypical pipeline for identifying, detecting, and addressing problematic autosuggestions. To showcase their complexity, we discuss several dimensions of problematic suggestions, difficult issues along the pipeline, and why our discussion applies to the increasing number of applications beyond web search that implement similar textual suggestion features. By outlining persistent social and technical challenges in moderating web search suggestions, we provide a renewed call for action.Comment: 17 Pages, 4 images displayed within 3 latex figure

On Suggesting Phrases vs. Predicting Words for Mobile Text Composition

A system capable of suggesting multi-word phrases while someone is writing could supply ideas about content and phrasing and allow those ideas to be inserted efficiently. Meanwhile, statistical language modeling has provided various approaches to predicting phrases that users type. We introduce a simple extension to the familiar mobile keyboard suggestion interface that presents phrase suggestions that can be accepted by a repeated-tap gesture. In an extended composition task, we found that phrases were interpreted as suggestions that affected the content of what participants wrote more than conventional single-word suggestions, which were interpreted as predictions. We highlight a design challenge: how can a phrase suggestion system make valuable suggestions rather than just accurate predictions

WiseType : a tablet keyboard with color-coded visualization and various editing options for error correction

To address the problem of improving text entry accuracy in mobile devices, we present a new tablet keyboard that offers both immediate and delayed feedback on language quality through auto-correction, prediction, and grammar checking. We combine different visual representations for grammar and spelling errors, accepted predictions, and auto-corrections, and also support interactive swiping/tapping features and improved interaction with previous errors, predictions, and auto-corrections. Additionally, we added smart error correction features to the system to decrease the overhead of correcting errors and to decrease the number of operations. We designed our new input method with an iterative user-centered approach through multiple pilots. We conducted a lab-based study with a refined experimental methodology and found that WiseType outperforms a standard keyboard in terms of text entry speed and error rate. The study shows that color-coded text background highlighting and underlining of potential mistakes in combination with fast correction methods can improve both writing speed and accuracy

The AI Ghostwriter Effect: When Users Do Not Perceive Ownership of AI-Generated Text But Self-Declare as Authors

Human-AI interaction in text production increases complexity in authorship. In two empirical studies (n1 = 30 & n2 = 96), we investigate authorship and ownership in human-AI collaboration for personalized language generation. We show an AI Ghostwriter Effect: Users do not consider themselves the owners and authors of AI-generated text but refrain from publicly declaring AI authorship. Personalization of AI-generated texts did not impact the AI Ghostwriter Effect, and higher levels of participants' influence on texts increased their sense of ownership. Participants were more likely to attribute ownership to supposedly human ghostwriters than AI ghostwriters, resulting in a higher ownership-authorship discrepancy for human ghostwriters. Rationalizations for authorship in AI ghostwriters and human ghostwriters were similar. We discuss how our findings relate to psychological ownership and human-AI interaction to lay the foundations for adapting authorship frameworks and user interfaces in AI in text-generation tasks.Comment: Pre-print; currently under revie

Typing Efficiency and Suggestion Accuracy Influence the Benefits and Adoption of Word Suggestions

International audienceSuggesting words to complete a given sequence of characters isa common feature of typing interfaces. Yet, previous studies havenot found a clear benefit, some even finding it detrimental. Wereport on the first study to control for two important factors, wordsuggestion accuracy and typing efficiency. Our accuracy factor isenabled by a new methodology that builds on standard metrics ofword suggestions. Typing efficiency is based on device type. Resultsshow word suggestions are used less often in a desktop condition,with little difference between tablet and phone conditions. Veryaccurate suggestions do not improve entry speed on desktop, but doon tablet and phone. Based on our findings, we discuss implicationsfor the design of automation features in typing systems