873 research outputs found
Crime pattern detection using online social media
In this research, we show online social networks can be used to study crime detection problems. Crime is defined as an act harmful not only to the individual involved, but also to the community as a whole. It is also a forbidden act that is punishable by law. Crimes are social nuisances that place heavy financial burdens on society. Here we look at use of data mining followed by sentiment analysis on online social networks, to help detect the crime patterns. Twitter is an online social networking and microblogging service that enables users to post brief text updates, also referred to as tweets . These updates can convey important information about the author. A filter was designed to extract tweets from cities deemed to be either the most dangerous or the safest in the United States (US). A geographic analysis revealed a correlation between these tweets and the crimes that occurred in the corresponding cities. Over 100,000 crime-related tweets were collected over a period of 20 days. Sentiment analysis techniques were conducted on these tweets to analyze the crime intensity of a particular location. This type of study will help reveal the crime rate of a location in real-time. Although the results of this test helped in detecting crime patterns, the sentiment analysis techniques did not always guarantee the proper results. We conclude with applications of this type of study and how it can be improved by applying media to text processing techniques --Abstract, page iii
A Comparative Study on Community Detection Methods in Complex Networks
Community detection aims to discover cohesive groups in which people connect with each other closely in social networks. A variety of methods have been proposed to detect communities in social networks. However, there is still few work to make a comparative study on those methods. In this paper, we first introduce and compare several representative methods on community detection. Then we implement those methods with python and make a comparative analysis on different real world social networking data sets. The experimental results have shown that GN algorithm is suitable for small networks, while LPA algorithm has a better scalability. FU algorithm is of the best stability. This work could help researchers to understand the ideas of community detection methods better and select appropriate method on demand more easily
Self-disclosure model for classifying & predicting text-based online disclosure
Les médias sociaux et les sites de réseaux sociaux sont devenus des babillards numériques pour les internautes à cause de leur évolution accélérée. Comme ces sites encouragent les consommateurs à exposer des informations personnelles via des profils et des publications, l'utilisation accrue des médias sociaux a généré des problèmes d’invasion de la vie privée. Des chercheurs ont fait de nombreux efforts pour détecter l'auto-divulgation en utilisant des techniques d'extraction d'informations. Des recherches récentes sur l'apprentissage automatique et les méthodes de traitement du langage naturel montrent que la compréhension du sens contextuel des mots peut entraîner une meilleure précision que les méthodes d'extraction de données traditionnelles.
Comme mentionné précédemment, les utilisateurs ignorent souvent la quantité d'informations personnelles publiées dans les forums en ligne. Il est donc nécessaire de détecter les diverses divulgations en langage naturel et de leur donner le choix de tester la possibilité de divulgation avant de publier.
Pour ce faire, ce travail propose le « SD_ELECTRA », un modèle de langage spécifique au contexte. Ce type de modèle détecte les divulgations d'intérêts, de données personnelles, d'éducation et de travail, de relations, de personnalité, de résidence, de voyage et d'accueil dans les données des médias sociaux. L'objectif est de créer un modèle linguistique spécifique au contexte sur une plate-forme de médias sociaux qui fonctionne mieux que les modèles linguistiques généraux.
De plus, les récents progrès des modèles de transformateurs ont ouvert la voie à la formation de modèles de langage à partir de zéro et à des scores plus élevés. Les résultats expérimentaux montrent que SD_ELECTRA a surpassé le modèle de base dans toutes les métriques considérées pour la méthode de classification de texte standard. En outre, les résultats montrent également que l'entraînement d'un modèle de langage avec un corpus spécifique au contexte de préentraînement plus petit sur un seul GPU peut améliorer les performances.
Une application Web illustrative est conçue pour permettre aux utilisateurs de tester les possibilités de divulgation dans leurs publications sur les réseaux sociaux. En conséquence, en utilisant l'efficacité du modèle suggéré, les utilisateurs pourraient obtenir un apprentissage en temps réel sur l'auto-divulgation.Social media and social networking sites have evolved into digital billboards for internet users due to their rapid expansion. As these sites encourage consumers to expose personal information via profiles and postings, increased use of social media has generated privacy concerns. There have been notable efforts from researchers to detect self-disclosure using Information extraction (IE) techniques. Recent research on machine learning and natural language processing methods shows that understanding the contextual meaning of the words can result in better accuracy than traditional data extraction methods.
Driven by the facts mentioned earlier, users are often ignorant of the quantity of personal information published in online forums, there is a need to detect various disclosures in natural language and give them a choice to test the possibility of disclosure before posting.
For this purpose, this work proposes "SD_ELECTRA," a context-specific language model to detect Interest, Personal, Education and Work, Relationship, Personality, Residence, Travel plan, and Hospitality disclosures in social media data. The goal is to create a context-specific language model on a social media platform that performs better than the general language models.
Moreover, recent advancements in transformer models paved the way to train language models from scratch and achieve higher scores. Experimental results show that SD_ELECTRA has outperformed the base model in all considered metrics for the standard text classification method. In addition, the results also show that training a language model with a smaller pre-training context-specific corpus on a single GPU can improve its performance.
An illustrative web application designed allows users to test the disclosure possibilities in their social media posts. As a result, by utilizing the efficiency of the suggested model, users would be able to get real-time learning on self-disclosure
Understanding evolution of customers' expectations on Finnish Hotels
Hotel industry is a special characterized industry which is immaterial, non-storable, non-transportable and always include integration of external factors. Therefore, worth of mouth (WOM) or electronic-WOM is considered the most important reference in guests’ decision making process of hotel brand selection. Thanks to the development of user-generated-content (UGC) platform, guest users have been expanding their roles from information receivers to active content creators. That makes the voice of customers more remarkable and crucial than ever. Although many studies have been conducted in understanding customer behavior, there are gaps between customer expectation and hotelier perspective. The purpose of this study was to investigate online reviews from the guests of Helsinki hotels in order to identify their evolving expectations.
Customer expectations on hotel service are believed to be evolving with time. Nonetheless, there is a lack of studies investigating how hotel customers’ expectations evolve with time. In this vein, this thesis investigated the changes in the most important topics and their related keywords that are manifested in online hotel reviews at different years. This study employed keywords extraction and sentiment analysis approaches pertaining to the methods such as POS tagging, N-gram and word frequency analysis.
This research offers both academic and practical implications. For academics, the mining framework can be applied in many different industries. This can be considered as the antecedence of further automatic mining model such as co-occurrence analysis. Practically, the findings confirm most important hotel attributes such as “room” “breakfast” “location” “staff”, “cleanliness”, etc. The results revealed some interesting changes in customer expectations on hotel service. For instance example, new keyword “wifi” is replacing the presentation of old keywords “tv” and “internet”. These replacement prove the clear evolution of customer expectation that need to be concentrated by hotelier
Adopting Home Language and Multimodality in Composition Courses
Over the years, language has been a major issue in teaching composition courses, specifically when discussing African American Vernacular English (AAVE) and Standard English (SE). Concepts such as Students Right to their Own Language (SRTOL), culturally relevant pedagogy, and code-switching have been introduced as ways to be more receptive to home language in the classroom. However, many students still lack feeling confidence to expressing themselves in their natural voices. I conducted this study to examine and tests how well AAVE, SE, code-meshing, and multimodality work together to help students better understand linguistic and rhetorical principles. This study found that teacher efficacy works to teach students how to comfortably and confidently navigate different communication spaces and help them to retain their home identities. My study bridges the gap in research by connecting home language studies, rhetoric and composition, and multimodal assignments in the composition classroom.
Using teacher research in my Composition II classroom at CAU, a Historically Black College and University in Atlanta , I conducted a mixed methods case study using code-meshing, sonic rhetoric, remix theory, and multimodal assignments in the classroom.
The study answered the following questions: 1) can multimodal assignments be used to teach students to think more about how they use Standard Written English (SWE)? 2) Does code-meshing assist students with using SWE more in assignments or in other contexts? and 3) can auditory rhetoric and techno-inclusionism impact how students approach composition? My study’s purpose was to see how students’ comfort, confidence, and concepts of race, language, identity, and fluency were impacted by the methods used. This dissertation explores the surveys and assignments used to collect data and the results that culminated from it
Role of Semantic web in the changing context of Enterprise Collaboration
In order to compete with the global giants, enterprises are concentrating on
their core competencies and collaborating with organizations that compliment their
skills and core activities. The current trend is to develop temporary alliances of
independent enterprises, in which companies can come together to share skills, core
competencies and resources. However, knowledge sharing and communication
among multidiscipline companies is a complex and challenging problem. In a
collaborative environment, the meaning of knowledge is drastically affected by the
context in which it is viewed and interpreted; thus necessitating the treatment of
structure as well as semantics of the data stored in enterprise repositories. Keeping
the present market and technological scenario in mind, this research aims to propose
tools and techniques that can enable companies to assimilate distributed information
resources and achieve their business goals
Integration of Technology Within Intervention Strategies for Students With High Functioning Autism: A Phenomenological Approach to Analyzing Educators’ Viewpoints
There is a phenomenon that exists within the Maryland State Public School System regarding technology integration within intervention strategies for students with high functioning autism (HFA). Educators have attested that there is minimally available technology for consistent use when working with their students during intervention strategies and services. Thus, when stakeholders understand the actual experiences of the professionals that work with students that have HFA on a daily basis, positive reform may occur at the immediate level by administrators within school buildings. The purpose of this study was to examine how general and special educators experienced technology use during interventions that they provided to their students with HFA. There were two main research questions: How do general and special educators describe their experiences using technology during interventions for students with HFA? What factors are IEP team committee members considering when they decide to include or refrain from adding technology accommodations within an IEP for students with HFA? The instrumentation utilized in this study was a set of open-ended questions conducted in an interview format. After careful analysis of the data collected, six main themes were detected connected to the conceptual framework of educational equity, persuasive technology, and theory of mind. The necessity of serious funding reform for technology within this particular county are the implications for future practices in the Maryland State public school system. Provision of technology including electronic devices, adequate professional development, and increased funding will equalize educational access for disabled students with HFA
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