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In this paper, we explore how children engage with search engine result pages (SERP) generated by a popular search API in response to their online inquiries. We do so to further understand children navigation behaviour. To accomplish this goal, we examine search logs produced as a result of children (ages 6 to 12), using metrics commonly used to operationalize engagement, including: position of clicks, time spent hovering, and the sequence of navigation on a SERP. We also investigate the potential connection between the text complexity of SERP snippets and engagement. Our findings verify that children engage more frequently with SERP results in higher ranking positions, but that engagement does not decrease linearly as children navigate to lower ranking positions. They also reveal that children generally spend more time hovering on snippets with more complex readability levels (i.e., harder to read) than snippets on the lower end of the readability spectrum. 

Given the more widespread nature of natural language interfaces, it is increasingly important to understand who are accessing those interfaces, and how those interfaces are being used. In this paper, we explore spellchecking in the context of web search with children as the target audience. In particular, via a literature review we show that, while widely used, popular search tools are ill-designed for children. We then use spellcheckers as a case study to highlight the need for an interdisciplinary approach that brings together natural language processing, education, human-computer interaction to address a known information retrieval problem: query misspelling. We conclude that it is imperative that those for whom the interfaces are designed have a voice in the design process. 

In this paper, we take a step towards understanding how to design search engine results pages (SERP) that encourage children’s engagement as they seek for online resources. For this, we conducted a participatory design session to enable us to elicit children’s preferences and determine what children (ages 6–12) find lacking in more traditional SERP. We learned that children want more dynamic means of navigating results and additional ways to interact with results via icons. We use these findings to inform the design of a new SERP interface, which we denoted CHIRP. To gauge the type of engagement that a SERP incorporating interactive elements–CHIRP–can foster among children, we conducted a user study at a public school. Analysis of children’s interactions with CHIRP, in addition to responses to a post-task survey, reveals that adding additional interaction points results in a SERP interface that children prefer, but one that does not necessarily change engagement levels through clicks or time spent on SERP. 

Spellchecking functionality embedded in existing search tools can assist children by offering a list of spelling alternatives when a spelling error is detected. Unfortunately, children tend to generally select the first alternative when presented with a list of options, as opposed to the one that matches their intent. In this paper, we describe a study we conducted with 191 children ages 6-12 in order to offer empirical evidence of: (1) their selection habits when identifying spelling suggestions that match the word they meant to type, and (2) the degree of influence multimodal cues, i.e., synthesized speech and images, have in prompting children to select the correct spelling suggestion. The results from our study reveal that multimodal cues, primarily synthesized speech, have a positive impact on the children's ability to identify their intended word from a list of spelling suggestions. 

An increasing number of people are sharing information through text messages, emails, and social media without proper privacy checks. In many situations, this could lead to serious privacy threats. This paper presents a methodology for providing extra safety precautions without being intrusive to users. We have developed and evaluated a model to help users take control of their shared information by automatically identifying text (i.e., a sentence or a transcribed utterance) that might contain personal or private disclosures. We apply off-the-shelf natural language processing tools to derive linguistic features such as part-of-speech, syntactic dependencies, and entity relations. From these features, we model and train a multichannel convolutional neural network as a classifier to identify short texts that have personal, private disclosures. We show how our model can notify users if a piece of text discloses personal or private information, and evaluate our approach in a binary classification task with 93% accuracy on our own labeled dataset, and 86% on a dataset of ground truth. Unlike document classification tasks in the area of natural language processing, our framework is developed keeping the sentence level context into consideration. 

As children search the internet for materials, they o en turn to search engines that, unfortunately, o er children li le support as they formulate queries to initiate the search process or examine resources for relevance. While some solutions have been proposed to address this, inherent to this issue is the need to evaluate the e ectiveness of these solutions. We posit that the evaluation of the diverse aspects involved in the search process – from query suggestion generation to resource retrieval – requires a complex, multi-faceted approach that draws on evaluation methods utilized in human-computer interaction, information retrieval, natural language processing, education, and psychology. 

Children use popular web search tools, which are generally designed for adult users. Because children have different developmental needs than adults, these tools may not always adequately support their search for information. Moreover, even though search tools offer support to help in query formulation, these too are aimed at adults and may hinder children rather than help them. This calls for the examination of existing technologies in this area, to better understand what remains to be done when it comes to facilitating query-formulation tasks for young users. In this paper, we investigate interaction elements of query formulation--including query suggestion algorithms--for children. The primary goals of our research efforts are to: (i) examine existing plug-ins and interfaces that explicitly aid children's query formulation; (ii) investigate children's interactions with suggestions offered by a general-purpose query suggestion strategy vs. a counterpart designed with children in mind; and (iii) identify, via participatory design sessions, their preferences when it comes to tools / strategies that can help children find information and guide them through the query formulation process. Our analysis shows that existing tools do not meet children's needs and expectations; the outcomes of our work can guide researchers and developers as they implement query formulation strategies for children. 

Misspellings in queries used to initiate online searches is an everyday occurrence. When this happens, users either rely on the search engine's ability to understand their query or they turn to spellcheckers. Spellcheckers are usually based on popular dictionaries or past query logs, leading to spelling suggestions that often better resonate with adult users because that data is more readily available. Based on an educational perspective, previous research reports, and initial analyses of sample search logs, we hypothesize that existing spellcheckers are not suitable for young users who frequently encounter spelling challenges when searching for information online. We present early results of our ongoing research focused on identifying the needs and expectations children have regarding spellcheckers.