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- Creators: Arizona State University
- Creators: Tong, Hanghang
Description
ABSTRACT Cyberbullying has emerged as one of educators' and researchers' chief concerns as the use of computer mediated communication (CMC) has become ubiquitous among young people. Many undesirable outcomes have been identified as being linked to both traditional and cyberbullying, including depression,truancy, and suicide. America and Japan have both been identified as nations whose youth engage frequently in the use of CMC, and may be at a potentially higher risk to be involved in cyberbullying. Time spent using CMC has been linked to involvement in cyberbullying, and gender and age have, in turn, been linked to CMC use - these may play significant roles in determining who is at risk. In order to assess the effects of nationality, gender, and age on cyberbullying involvement among Japanese and American middle school students, a survey exploring these factors was developed and carried out with 590 American and Japanese middles school students (Japan: n = 433 and America: n = 157). MANOVA results indicated that that Americans tend to both use CMC more and be more involved in cyberbullying. In addition, Japanese involvement increased with age, while American involvement did not. There were minimal differences between Americans and Japanese with regards to traditional bullying.
ContributorsLerner, David (Author) / Nakagawa, Kathryn (Thesis advisor) / Caterino, Linda (Thesis advisor) / Ladd, Becky (Committee member) / Arizona State University (Publisher)
Created2011
Description
Bridging semantic gap is one of the fundamental problems in multimedia computing and pattern recognition. The challenge of associating low-level signal with their high-level semantic interpretation is mainly due to the fact that semantics are often conveyed implicitly in a context, relying on interactions among multiple levels of concepts or low-level data entities. Also, additional domain knowledge may often be indispensable for uncovering the underlying semantics, but in most cases such domain knowledge is not readily available from the acquired media streams. Thus, making use of various types of contextual information and leveraging corresponding domain knowledge are vital for effectively associating high-level semantics with low-level signals with higher accuracies in multimedia computing problems. In this work, novel computational methods are explored and developed for incorporating contextual information/domain knowledge in different forms for multimedia computing and pattern recognition problems. Specifically, a novel Bayesian approach with statistical-sampling-based inference is proposed for incorporating a special type of domain knowledge, spatial prior for the underlying shapes; cross-modality correlations via Kernel Canonical Correlation Analysis is explored and the learnt space is then used for associating multimedia contents in different forms; model contextual information as a graph is leveraged for regulating interactions among high-level semantic concepts (e.g., category labels), low-level input signal (e.g., spatial/temporal structure). Four real-world applications, including visual-to-tactile face conversion, photo tag recommendation, wild web video classification and unconstrained consumer video summarization, are selected to demonstrate the effectiveness of the approaches. These applications range from classic research challenges to emerging tasks in multimedia computing. Results from experiments on large-scale real-world data with comparisons to other state-of-the-art methods and subjective evaluations with end users confirmed that the developed approaches exhibit salient advantages, suggesting that they are promising for leveraging contextual information/domain knowledge for a wide range of multimedia computing and pattern recognition problems.
ContributorsWang, Zhesheng (Author) / Li, Baoxin (Thesis advisor) / Sundaram, Hari (Committee member) / Qian, Gang (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2011
Description
Text classification, in the artificial intelligence domain, is an activity in which text documents are automatically classified into predefined categories using machine learning techniques. An example of this is classifying uncategorized news articles into different predefined categories such as "Business", "Politics", "Education", "Technology" , etc. In this thesis, supervised machine learning approach is followed, in which a module is first trained with pre-classified training data and then class of test data is predicted. Good feature extraction is an important step in the machine learning approach and hence the main component of this text classifier is semantic triplet based features in addition to traditional features like standard keyword based features and statistical features based on shallow-parsing (such as density of POS tags and named entities). Triplet {Subject, Verb, Object} in a sentence is defined as a relation between subject and object, the relation being the predicate (verb). Triplet extraction process, is a 5 step process which takes input corpus as a web text document(s), each consisting of one or many paragraphs, from RSS feeds to lists of extremist website. Input corpus feeds into the "Pronoun Resolution" step, which uses an heuristic approach to identify the noun phrases referenced by the pronouns. The next step "SRL Parser" is a shallow semantic parser and converts the incoming pronoun resolved paragraphs into annotated predicate argument format. The output of SRL parser is processed by "Triplet Extractor" algorithm which forms the triplet in the form {Subject, Verb, Object}. Generalization and reduction of triplet features is the next step. Reduced feature representation reduces computing time, yields better discriminatory behavior and handles curse of dimensionality phenomena. For training and testing, a ten- fold cross validation approach is followed. In each round SVM classifier is trained with 90% of labeled (training) data and in the testing phase, classes of remaining 10% unlabeled (testing) data are predicted. Concluding, this paper proposes a model with semantic triplet based features for story classification. The effectiveness of the model is demonstrated against other traditional features used in the literature for text classification tasks.
ContributorsKarad, Ravi Chandravadan (Author) / Davulcu, Hasan (Thesis advisor) / Corman, Steven (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2013
Description
ABSTRACT The phenomenon of cyberbullying has captured the attention of educators and researchers alike as it has been associated with multiple aversive outcomes including suicide. Young people today have easy access to computer mediated communication (CMC) and frequently use it to harass one another -- a practice that many researchers have equated to cyberbullying. However, there is great disagreement among researchers whether intentional harmful actions carried out by way of CMC constitute cyberbullying, and some authors have argued that "cyber-aggression" is a more accurate term to describe this phenomenon. Disagreement in terms of cyberbullying's definition and methodological inconsistencies including choice of questionnaire items has resulted in highly variable results across cyberbullying studies. Researchers are in agreement however, that cyber and traditional forms of aggression are closely related phenomena, and have suggested that they may be extensions of one another. This research developed a comprehensive set of items to span cyber-aggression's content domain in order to 1) fully address all types of cyber-aggression, and 2) assess the interrelated nature of cyber and traditional aggression. These items were administered to 553 middle school students located in a central Illinois school district. Results from confirmatory factor analyses suggested that cyber-aggression is best conceptualized as integrated with traditional aggression, and that cyber and traditional aggression share two dimensions: direct-verbal and relational aggression. Additionally, results indicated that all forms of aggression are a function of general aggressive tendencies. This research identified two synthesized models combining cyber and traditional aggression into a shared framework that demonstrated excellent fit to the item data.
ContributorsLerner, David (Author) / Green, Samuel B (Thesis advisor) / Caterino, Linda (Committee member) / Atkinson, Robert (Committee member) / Nakagawa, Kathryn (Committee member) / Arizona State University (Publisher)
Created2013
Description
The present study was designed to extend previous research on early adolescents' involvement in electronic aggression and victimization. A new measure for electronic victimization and aggression was created for this study in order to better assess this type of peer harassment in early adolescence. The first goal of the study was to describe young adolescents' involvement in electronic aggression and victimization by exploring the links between electronic victimization and aggression and (a) youth demographic characteristics (e.g., gender, ethnicity), (b) involvement in traditional forms of aggression and victimization, and (c) gender of the aggression/victimization context (i.e., same-sex aggressor -victim versus other-sex aggressor- victim dyad). The second goal was to examine how electronic victimization and aggression were associated with self-esteem and relationship efficacy. Participants were 826 (49.9% female) 7th and 8th grade students (M age = 12.5 years old; SD = .67). Students were administered surveys during school hours. Results indicated that girls were more likely to be involved in both electronic aggression and victimization than boys. Further, girls were more likely to be both electronic aggressors and victims simultaneously than boys. Finally, those involved with electronic aggression reported higher levels of relationship efficacy than their peers and involvement as an aggressor/victim was associated with lower self-esteem than any other involvement category.
ContributorsMartin, Melissa (Author) / Updegraff, Kimberly A (Thesis advisor) / Ladd, Becky (Committee member) / Martin, Carol (Committee member) / Arizona State University (Publisher)
Created2013
Description
This research addressed concerns regarding the measurement of cyberbullying and aimed to develop a reliable and valid measure of cyberbullying perpetration and victimization. Despite the growing body of literature on cyberbullying, several measurement concerns were identified and addressed in two pilot studies. These concerns included the most appropriate time frame for behavioral recall, use of the term "cyberbullying" in questionnaire instructions, whether to refer to power in instances of cyberbullying, and best practices for designing self-report measures to reflect how young adults understand and communicate about cyberbullying. Mixed methodology was employed in two pilot studies to address these concerns and to determine how to best design a measure which participants could respond to accurately and honestly. Pilot study one consisted of an experimental examination of time frame for recall and use of the term on the outcomes of honesty, accuracy, and social desirability. Pilot study two involved a qualitative examination of several measurement concerns through focus groups held with young adults. Results suggested that one academic year was the most appropriate time frame for behavioral recall, to avoid use of the term "cyberbullying" in questionnaire instructions, to include references to power, and other suggestions for the improving the method in the main study to bolster participants' attention. These findings informed the development of a final measure in the main study which aimed to be both practical in its ability to capture prevalence and precise in its ability to measure frequency. The main study involved examining the psychometric properties, reliability, and validity of the final measure. Results of the main study indicated that the final measure exhibited qualities of an index and was assessed as such. Further, structural equation modeling techniques and test-retest procedures indicated the measure had good reliability. And, good predictive validity and satisfactory convergent validity was established for the final measure. Results derived from the measure concerning prevalence, frequency, and chronicity are presented within the scope of findings in cyberbullying literature. Implications for practice and future directions for research with the measure developed here are discussed.
ContributorsSavage, Matthew (Author) / Roberto, Anthony J (Thesis advisor) / Palazzolo, Kellie E (Committee member) / Thompson, Marilyn S (Committee member) / Arizona State University (Publisher)
Created2012
Description
In the current millennium, extensive use of computers and the internet caused an exponential increase in information. Few research areas are as important as information extraction, which primarily involves extracting concepts and the relations between them from free text. Limitations in the size of training data, lack of lexicons and lack of relationship patterns are major factors for poor performance in information extraction. This is because the training data cannot possibly contain all concepts and their synonyms; and it contains only limited examples of relationship patterns between concepts. Creating training data, lexicons and relationship patterns is expensive, especially in the biomedical domain (including clinical notes) because of the depth of domain knowledge required of the curators. Dictionary-based approaches for concept extraction in this domain are not sufficient to effectively overcome the complexities that arise because of the descriptive nature of human languages. For example, there is a relatively higher amount of abbreviations (not all of them present in lexicons) compared to everyday English text. Sometimes abbreviations are modifiers of an adjective (e.g. CD4-negative) rather than nouns (and hence, not usually considered named entities). There are many chemical names with numbers, commas, hyphens and parentheses (e.g. t(3;3)(q21;q26)), which will be separated by most tokenizers. In addition, partial words are used in place of full words (e.g. up- and downregulate); and some of the words used are highly specialized for the domain. Clinical notes contain peculiar drug names, anatomical nomenclature, other specialized names and phrases that are not standard in everyday English or in published articles (e.g. "l shoulder inj"). State of the art concept extraction systems use machine learning algorithms to overcome some of these challenges. However, they need a large annotated corpus for every concept class that needs to be extracted. A novel natural language processing approach to minimize this limitation in concept extraction is proposed here using distributional semantics. Distributional semantics is an emerging field arising from the notion that the meaning or semantics of a piece of text (discourse) depends on the distribution of the elements of that discourse in relation to its surroundings. Distributional information from large unlabeled data is used to automatically create lexicons for the concepts to be tagged, clusters of contextually similar words, and thesauri of distributionally similar words. These automatically generated lexical resources are shown here to be more useful than manually created lexicons for extracting concepts from both literature and narratives. Further, machine learning features based on distributional semantics are shown to improve the accuracy of BANNER, and could be used in other machine learning systems such as cTakes to improve their performance. In addition, in order to simplify the sentence patterns and facilitate association extraction, a new algorithm using a "shotgun" approach is proposed. The goal of sentence simplification has traditionally been to reduce the grammatical complexity of sentences while retaining the relevant information content and meaning to enable better readability for humans and enhanced processing by parsers. Sentence simplification is shown here to improve the performance of association extraction systems for both biomedical literature and clinical notes. It helps improve the accuracy of protein-protein interaction extraction from the literature and also improves relationship extraction from clinical notes (such as between medical problems, tests and treatments). Overall, the two main contributions of this work include the application of sentence simplification to association extraction as described above, and the use of distributional semantics for concept extraction. The proposed work on concept extraction amalgamates for the first time two diverse research areas -distributional semantics and information extraction. This approach renders all the advantages offered in other semi-supervised machine learning systems, and, unlike other proposed semi-supervised approaches, it can be used on top of different basic frameworks and algorithms.
ContributorsJonnalagadda, Siddhartha Reddy (Author) / Gonzalez, Graciela H (Thesis advisor) / Cohen, Trevor A (Committee member) / Greenes, Robert A (Committee member) / Fridsma, Douglas B (Committee member) / Arizona State University (Publisher)
Created2011
Description
Continuous advancements in biomedical research have resulted in the production of vast amounts of scientific data and literature discussing them. The ultimate goal of computational biology is to translate these large amounts of data into actual knowledge of the complex biological processes and accurate life science models. The ability to rapidly and effectively survey the literature is necessary for the creation of large scale models of the relationships among biomedical entities as well as hypothesis generation to guide biomedical research. To reduce the effort and time spent in performing these activities, an intelligent search system is required. Even though many systems aid in navigating through this wide collection of documents, the vastness and depth of this information overload can be overwhelming. An automated extraction system coupled with a cognitive search and navigation service over these document collections would not only save time and effort, but also facilitate discovery of the unknown information implicitly conveyed in the texts. This thesis presents the different approaches used for large scale biomedical named entity recognition, and the challenges faced in each. It also proposes BioEve: an integrative framework to fuse a faceted search with information extraction to provide a search service that addresses the user's desire for "completeness" of the query results, not just the top-ranked ones. This information extraction system enables discovery of important semantic relationships between entities such as genes, diseases, drugs, and cell lines and events from biomedical text on MEDLINE, which is the largest publicly available database of the world's biomedical journal literature. It is an innovative search and discovery service that makes it easier to search
avigate and discover knowledge hidden in life sciences literature. To demonstrate the utility of this system, this thesis also details a prototype enterprise quality search and discovery service that helps researchers with a guided step-by-step query refinement, by suggesting concepts enriched in intermediate results, and thereby facilitating the "discover more as you search" paradigm.
avigate and discover knowledge hidden in life sciences literature. To demonstrate the utility of this system, this thesis also details a prototype enterprise quality search and discovery service that helps researchers with a guided step-by-step query refinement, by suggesting concepts enriched in intermediate results, and thereby facilitating the "discover more as you search" paradigm.
ContributorsKanwar, Pradeep (Author) / Davulcu, Hasan (Thesis advisor) / Dinu, Valentin (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2010
Description
Background: Cyberbullying and cyber-victimization are rising problems and are associated with increased risk for mental health problems in children. Methods for addressing cyberbullying are limited, however, interventions focused on promoting appropriate parental mediation strategies are a promising solution supported by evidence and by guided by the Theory of Parenting Styles.
Objective: To provide an educational session to parents of middle school students that promotes effective methods of preventing and addressing cyberbullying incidents. Design: The educational sessions were provided to eight parents middle school student. Surveys to assess parent perception of and planned response to cyberbullying incidents and Parent Adolescent Communication Scale (PACS) scores were collected pre-presentation, post-presentation, and at one-month follow up.
Results: Data analysis of pre- and post-presentation PACS using a Wilcoxon test found no significant difference (Z = -.405, p >.05). There was not enough response to the 1-month follow-up to perform a data analysis on follow-up data.
Conclusions: Due to low attendance and participation in the follow-up survey the results of this project are limited. However, parents did appear to benefit from communicating concerns about cyberbullying with school officials. Future studies should examine if a school-wide anti-cyberbullying program that actively involves parents effects parental response to cyberbullying.
ContributorsKelsy, Streeter (Author) / Guthrey, Ann (Thesis advisor)
Created2017-04-30
Description
Text mining of biomedical literature and clinical notes is a very active field of research in biomedical science. Semantic analysis is one of the core modules for different Natural Language Processing (NLP) solutions. Methods for calculating semantic relatedness of two concepts can be very useful in solutions solving different problems such as relationship extraction, ontology creation and question / answering [1–6]. Several techniques exist in calculating semantic relatedness of two concepts. These techniques utilize different knowledge sources and corpora. So far, researchers attempted to find the best hybrid method for each domain by combining semantic relatedness techniques and data sources manually. In this work, attempts were made to eliminate the needs for manually combining semantic relatedness methods targeting any new contexts or resources through proposing an automated method, which attempted to find the best combination of semantic relatedness techniques and resources to achieve the best semantic relatedness score in every context. This may help the research community find the best hybrid method for each context considering the available algorithms and resources.
ContributorsEmadzadeh, Ehsan (Author) / Gonzalez, Graciela (Thesis advisor) / Greenes, Robert (Committee member) / Scotch, Matthew (Committee member) / Arizona State University (Publisher)
Created2016