The Digital Flynn Effect: Complexity of Posts on Social Media Increases over Time

Parents and teachers often express concern about the extensive use of social media by youngsters. Some of them see emoticons, undecipherable initialisms and loose grammar typical for social media as evidence of language degradation. In this paper, we use a simple measure of text complexity to investigate how the complexity of public posts on a popular social networking site changes over time. We analyze a unique dataset that contains texts posted by 942, 336 users from a large European city across nine years. We show that the chosen complexity measure is correlated with the academic performance of users: users from high-performing schools produce more complex texts than users from low-performing schools. We also find that complexity of posts increases with age. Finally, we demonstrate that overall language complexity of posts on the social networking site is constantly increasing. We call this phenomenon the digital Flynn effect. Our results may suggest that the worries about language degradation are not warranted

Ambiguous keyboards for AAC

Purpose – “Ambiguous keyboards” and “disambiguation processes” are becoming universally recognised through the popularisation of “predictive text messaging” on mobile phones. As this paper shows, although originating in the AT and AAC fields, these terms and techniques no longer appear to be widely understood or adopted by practitioners or users. The purpose of this paper is to introduce these techniques, discussing the research and theory around them, and to suggest them as AT and AAC strategies to be considered by practitioners and users. Design/methodology/approach – This is a conceptual paper that describes the use of ambiguous keyboards and disambiguation. The hypothesis of the paper is that ambiguous keyboards and disambiguation processes offer potential to increase the efficiency and effectiveness of AAC and should thus be considered further in research and practice. Findings – The two broad methods for removing the ambiguity from the output of an ambiguous keyboard are presented. A summary of the literature around the use of disambiguation processes provided and the use of disambiguation processes for AAC discussed. Originality/value – This paper suggests that ambiguity should be adopted as a characteristic of an AAC keyboard as should the method of removing ambiguity – namely either coding or a disambiguation process

A Machine Learning Approach to Predicting Alcohol Consumption in Adolescents From Historical Text Messaging Data

Techniques based on artificial neural networks represent the current state-of-the-art in machine learning due to the availability of improved hardware and large data sets. Here we employ doc2vec, an unsupervised neural network, to capture the semantic content of text messages sent by adolescents during high school, and encode this semantic content as numeric vectors. These vectors effectively condense the text message data into highly leverageable inputs to a logistic regression classifier in a matter of hours, as compared to the tedious and often quite lengthy task of manually coding data. Using our machine learning approach, we are able to train a logistic regression model to predict adolescents\u27 engagement in substance abuse during distinct life phases with accuracy ranging from 76.5% to 88.1%. We show the effects of grade level and text message aggregation strategy on the efficacy of document embedding generation with doc2vec. Additional examination of the vectorizations for specific terms extracted from the text message data adds quantitative depth to this analysis. We demonstrate the ability of the method used herein to overcome traditional natural language processing concerns related to unconventional orthography. These results suggest that the approach described in this thesis is a competitive and efficient alternative to existing methodologies for predicting substance abuse behaviors. This work reveals the potential for the application of machine learning-based manipulation of text messaging data to development of automatic intervention strategies against substance abuse and other adolescent challenges

The Past, Present and Future of the English Language: How Has the English Language Changed and What Effects Are Going to Come as a Result of Texting?

This paper outlines a brief historical synopsis of both language in general and the English language to set up a common knowledge baseline for the reader to understand references made regarding historical events. Next, the paper goes through common practices seen today within the realm of text messages, textisms, text speak, and the lingo used within these practices. Common practices within the technology based language and standardized English are analyzed to compare and contrast the two forms and to help answer the question of whether or not technology is harming the English language and its practices. Findings provide evidence that there are indeed similarities between the two forms and that there is a strong relation between informal spoken language and text speak. The final portion of the paper is devoted to the future of the language and how the language is developing. Interestingly enough, many of the practices used within text speak currently are very similar to that of ancient practices. There is also a look into academia along with looking at if and how the use of technology and the lingo that comes with it is affecting students and their literacy abilities. Results were varied and researchers found a hard time coming to a consensus but there were significant negative correlations along with positive correlations.Kayla SiddellHonors DiplomaHonors CollegeCunningham Memorial Library, Terre Haute, Indiana State UniversityUndergraduateTitle from document title page. Document formatted into pages: 42