Extending Safe Search Functionality for Identifying Child-Safe and Educational Web Resources

Safe search is a filtering strategy used by search engines for the purpose of preventing children from accessing web resources that either contain adult content (i.e., pornography and nudity) or promote violence (i.e., include hate-speech and offensive language). Unfortunately, safe search is not always the perfect deterrent: at times, pornographic and hate-based resources slip through the filter, whereas, other times, resources that may be relevant to a child’s educational search context are misconstrued as being inappropriate, and are therefore filtered. In this paper, we first examine the functionality of a number of existing safe search filters. Based on our findings, we present ongoing efforts to address some of the limitations with traditional safe search filtering strategies

Investigating Query Formulation Assistance for Children

Popular tools used to search for online resources are tuned to satisfy a broad category of users—primarily adults. Because children have specific needs, these tools may not always be successful in offering the right level of support in their quest for information. While search tools often provide query assistance, children still face many difficulties expressing their information needs in the form of a query. In this paper, we share results from our ongoing research work focused on understanding children\u27s interactions with query suggestions and their preferences with respect to suggestions offered by a general-purpose strategy versus a counterpart designed exclusively for children. Our goal is to inform researchers and developers about when it is necessary to turn to technologies tailored exclusively for children and to further outline needs that should be addressed when it comes to designing query-formulation-related technology for children

Query Formulation Assistance for Kids: What is Available, When to Help & What Kids Want

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

Fostering the Retrieval of Suitable Web Resources in Response to Children\u27s Educational Search Tasks

Children regularly turn to search engines (SEs) to locate school-related materials. Unfortunately, research has shown that when utilizing SEs, children do not always access resources that specifically target them. To support children, popular and child-oriented SEs make available a safe search filter, which is meant to eliminate inappropriate resources. Safe search is, however, not always the perfect deterrent as pornographic and hate-based resources may slip through the filter, while resources relevant to an educational search context may be misconstrued and filtered out. Moreover, filtering inappropriate resources in response to children searches is just one perspective to consider in offering them the right resources, as aspects that are key for this audience are overlooked, including reading level, resource subjectivity, or the context of the search (i.e., educational setting). To verify impediments of existing SEs in response to children searches conducted at school, we conduct an empirical study on well known SEs: Google, Bing, their safe search counterparts, Kidrex and Kidzsearch. Based on our findings, we present KiSuRF, a novel filtering and ranking strategy that not only eliminates inappropriate resources while retaining education-relevant ones, but also simultaneously examines multiple qualitative aspects of online resources in order to offer suitable ones. Empirical studies conducted using diverse datasets, including one comprised of children search sessions in the school setting, showcase (i) the usefulness of simultaneously integrating evidences from multiple perspectives in order to inform resource suitability detection, and (ii) the correctness and effectiveness of KiSuRF in prioritizing child-suitable resources

An Empirical Analysis of Search Engines’ Response to Web Search Queries Associated with the Classroom Setting

Purpose – The purpose of this paper is to examine strengths and limitations that search engines (SEs) exhibit when responding to web search queries associated with the grade school curriculum Design/methodology/approach – The authors employed a simulation-based experimental approach to conduct an in-depth empirical examination of SEs and used web search queries that capture information needs in different search scenarios. Findings – Outcomes from this study highlight that child-oriented SEs are more effective than traditional ones when filtering inappropriate resources, but often fail to retrieve educational materials. All SEs examined offered resources at reading levels higher than that of the target audience and often prioritized resources with popular top-level domain (e.g. “.com”). Practical implications – Findings have implications for human intervention, search literacy in schools, and the enhancement of existing SEs. Results shed light on the impact on children’s education that result from introducing misconception about SEs when these tools either retrieve no results or offer irrelevant resources, in response to web search queries pertinent to the grade school curriculum. Originality/value – The authors examined child-oriented and popular SEs retrieval of resources aligning with task objectives and user capabilities–resources that match user reading skills, do not contain hate-speech and sexually-explicit content, are non-opinionated, and are curriculum-relevant. Findings identified limitations of existing SEs (both directly or indirectly supporting young users) and demonstrate the need to improve SE filtering and ranking algorithms

Data Set for An Empirical Analysis of Search Engines’ Response to Web Search Queries Associated with the Classroom Setting

This archive contains queries that capture information in different search contexts. The first file includes those written by children between the 3rd - 6th grade levels, while performing search tasks. We collected and archived this data between the April 2017 -- December 2018, based on Boise State University\u27s IRB approval. We also include simulated queries we extracted from children\u27s reviews. Additional columns in this dataset are children\u27s grade levels, the query source, and the query type (i.e., if it is a keyword, phrase, or question query). The other files are comprised of queries that are meant to lead to the retrieval of (1) educational, (2) sexually explicit, and (3) hate-based resources

Scripts for Can We Leverage Rating Patterns from Traditional Users to Enhance Recommendations for Children?

This archive contains the scripts to replicate the experiment for our paper [ Can we leverage rating patterns from traditional users to enhance recommendations for children? published in ACM RecSys by - Ion Madrazo Azpiazu, Michael Green, Oghenemaro Anuyah, and Maria Soledad Pera.] ### Requirements - Java - An R, Jupyter, Python, and Tidyverse installation. - The [MovieLens 1M](https://grouplens.org/datasets/movielens/) dataset, extracted into Data/input (you should have directory Data/ml-1m) - The Dogo dataset (or any dataset containing ratings provided by children) extracted into Data/input ### Instructions - Steps to run: - Install required software and data files enlisted in requirements. These directories and files should be present upon doing so: -Data/input/ml-1m (e.g. data/ml-1m/ratings.dat) -Data/input/Dogo (e.g., any children ratings file) - Run Jupyter notebook: - Create_Experimental_Datasets/Data_creation_notebook.ipynb - Run LibReC Experiment: - Input_Analysis/ - Visualize user-rating activity: - Rating_Distribution_Dogo_ML1M.ipynb ### On October 9, 2018 the downloaded zip file was revised to remove some lines of code that were intended for internal analyses only

A Ranking Strategy to Promote Resources Supporting the Classroom Environment

Popular search engines (SE) favored by children are optimized neither to respond to their search behavior and abilities, nor retrieve resources that align with classroom standards. Further, attempts to adapt SE to support children\u27s inquiries have been one-dimensional, e.g., satisfy users\u27 reading skills or search expertise. To ease the process of locating resources relevant to children in the classroom, we introduce KORSCE, a ranking strategy designed to complement the functionality of existing SE. KORSCE employs a multi-objective approach to re-rank resources retrieved by popular SE to fit a specific target audience and setting based on varied criteria: appropriateness, readability, objectivity, and curriculum-alignment. Experimental results and insights from an expert appraiser showcase KORSCE\u27S ability to prioritize resources that assist children\u27s information-seeking activities at school

KidSpell: A Child-Oriented, Rule-Based, Phonetic Spellchecker

For help with their spelling errors, children often turn to spellcheckers integrated in software applications like word processors and search engines. However, existing spellcheckers are usually tuned to the needs of traditional users (i.e., adults) and generally prove unsatisfactory for children. Motivated by this issue, we introduce KidSpell, an English spellchecker oriented to the spelling needs of children. KidSpell applies (i) an encoding strategy for mapping both misspelled words and spelling suggestions to their phonetic keys and (ii) a selection process that prioritizes candidate spelling suggestions that closely align with the misspelled word based on their respective keys. To assess the effectiveness of KidSpell, we compare the model’s performance against several popular, mainstream spellcheckers in a number of offline experiments using existing and novel datasets. The results of these experiments show that KidSpell outperforms existing spellcheckers, as it accurately prioritizes relevant spelling corrections when handling misspellings generated by children in both essay writing and online search tasks. As a byproduct of our study, we create two new datasets comprised of spelling errors generated by children from hand-written essays and web search inquiries, which we make available to the research community