63 research outputs found

    Graphs behind data: A network-based approach to model different scenarios

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    openAl giorno d’oggi, i contesti che possono beneficiare di tecniche di estrazione della conoscenza a partire dai dati grezzi sono aumentati drasticamente. Di conseguenza, la definizione di modelli capaci di rappresentare e gestire dati altamente eterogenei è un argomento di ricerca molto dibattuto in letteratura. In questa tesi, proponiamo una soluzione per affrontare tale problema. In particolare, riteniamo che la teoria dei grafi, e più nello specifico le reti complesse, insieme ai suoi concetti ed approcci, possano rappresentare una valida soluzione. Infatti, noi crediamo che le reti complesse possano costituire un modello unico ed unificante per rappresentare e gestire dati altamente eterogenei. Sulla base di questa premessa, mostriamo come gli stessi concetti ed approcci abbiano la potenzialità di affrontare con successo molti problemi aperti in diversi contesti. ​Nowadays, the amount and variety of scenarios that can benefit from techniques for extracting and managing knowledge from raw data have dramatically increased. As a result, the search for models capable of ensuring the representation and management of highly heterogeneous data is a hot topic in the data science literature. In this thesis, we aim to propose a solution to address this issue. In particular, we believe that graphs, and more specifically complex networks, as well as the concepts and approaches associated with them, can represent a solution to the problem mentioned above. In fact, we believe that they can be a unique and unifying model to uniformly represent and handle extremely heterogeneous data. Based on this premise, we show how the same concepts and/or approach has the potential to address different open issues in different contexts. ​INGEGNERIA DELL'INFORMAZIONEopenVirgili, Luc

    Investigation of social behaviour patterns using location-based data - a Melbourne case study

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    Location-based social networks such as Swarm provide a rich source of information on human behaviour and urban functions. Our analysis of data created by users who voluntarily used check-ins with a mobile application can give insight into a user's mobility and behaviour patterns. In this study, we used location-sharing data from Swarm to explore spatiotemporal, geo-temporal and behaviour patterns within the city of Melbourne. Moreover, we used several tools for different datasets. We used the MeaningCloud tool for sentiment analysis and the LIWC15 tool for psychometric analysis. Also, we employed SPSS software for the descriptive statistical analysis on check-in data to reveal meaningful trends and attain a deeper understanding of human behaviour patterns in the city. The results show that most people do not express strong negative or positive emotions in relation to the places they visit. Behaviour patterns vary based on gender. Furthermore, mobility patterns are different on different days of the week as well as at different times of a day but are not necessarily influenced by the weather

    The Defamation Injunction Meets the Prior Restraint Doctrine

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    This article maintains that, under defined circumstances, a judge should be able to grant an injunction that forbids the defendant’s proved defamation. It analyzes the common law of defamation, the constitutional prior restraint doctrine, the constitutional protection for defamation that stems from New York Times v. Sullivan, and injunctions and their enforcement. In Near v. Minnesota, the Supreme Court expanded protection for expression by adding an injunction to executive licensing as a prior restraint. Although the Near court circumscribed the injunction as a prior restraint, it approved criminal sanctions and damages judgment for defamation. An injunction that forbids the defendant’s tort of defamation tests Near and prior restraint doctrine as well as New York Times v. Sullivan. Defamation isn’t protected by the First Amendment. A prior restraint label, however, resembles a death sentence. This article maintains that such massive retaliation is overkill; it argues for a more nuanced approach that allows the judge to grant an anti-defamation injunction under limited circumstances. Paired state supreme court decisions frame the debate. In Balboa Island Village Inn v. Lemen in 2007, the California Supreme Court approved a targeted injunction that forbids a defendant from repeating proved defamation. The Texas Supreme Court’s 2014 decision in Kinney v. Barnes rejected an anti-defamation injunction. This article maintains that the anti-defamation injunction has outgrown outright bans under the prior restraint rule and the equitable Maxim that “Equity will not enjoin defamation.” It examines the New York Times v. Sullivan privileges in defamation, their tension between truth and falsity, and their limitations on compensatory and punitive damages. It tests the injunction against damages by examining Equitable doctrines: the inadequacy prerequisite-irreparable injury rule, the injunction as preventive relief, the temporary restraining order, the preliminary injunction, the injunction bond, the juryless injunction trial, the task of drafting an injunction that avoids vagueness and over-breadth, the use of motions to modify-dissolve an injunction, the declaratory judgment, and contempt, compensatory, coercive, or criminal, including the collateral bar rule. It weighs and evaluates important prior restraint scholarship, including Professor Emerson’s and Professor Blasi’s. The administration of the prior restraint doctrine has expanded its operation beyond the policy reasons that gave it birth. This article concludes that the differences between damages and an injunction don’t warrant different treatment. Influential scholars beginning with Roscoe Pound and including more recently Professors Redish, Jeffries, Schauer, and Ardia have eroded the prior restraint doctrine’s reasoning and application. A court may enjoin defendants’ expression that isn’t protected free speech; examples are obscenity and copyright infringement. Defamation is also a defendant’s expression that isn’t protected free speech. However, the procedure and substance of defamation must operate in light of the Sullivan Court’s defenses. The procedure leading to an anti-defamation injunction can be augmented by requiring prior notice, adversary adjudication, and narrow drafting. A properly adjudicated and drafted injunction that specifically forbids defendant’s defamation will prevent harmful defamatory torts without threatening free-speech values. This article closes by asking courts to suspend or qualify the prior restraint doctrine for defamation and to abolish the no-injunction Maxim outright

    The Defamation Injunction Meets the Prior Restraint Doctrine

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    In Near v. Minnesota, the Supreme Court added the injunction to executive licensing as a prior restraint. Although the Near court circumscribed the injunction as a prior restraint, it approved criminal sanctions and damages judgments. The prior restraint label resembles a death sentence. This article maintains that such massive retaliation is overkill. A judge’s injunction that forbids the defendant’s tort of defamation tests Near and prior restraint doctrine because defamation isn’t protected by the First Amendment. Arguing that the anti-defamation injunction has outgrown outright bans under the prior restraint rule and the equitable Maxim that “Equity will not enjoin defamation” turns out to be formidable. This article examines the Sullivan v. New York Times privileges in defamation, their tension between truth and falsity, and their limitations on compensatory and punitive damages. It tests the injunction against damages by examining several Equitable doctrines: the inadequacy prerequisite-irreparable injury rule, the injunction as preventive relief, the temporary restraining order, the preliminary injunction, the injunction bond, the juryless injunction trial, the task of drafting an injunction to avoids vagueness and over-breadth, the use of motions to modify-dissolve an injunction, and the declaratory judgment, and contempt, compensatory, coercive, or criminal, including the collateral bar rule. It weighs important prior restraint scholarship, including Professor Emerson’s and Professor Blasi’s. The administration of the prior restraint doctrines has expanded its operation beyond the policy reasons that gave it birth. This article concludes that the differences between damages and an injunction don’t warrant different treatment. In Balboa Island Village Inn v. Lemen, the California Supreme Court approved a targeted injunction that forbids a defendant from repeating proved defamation. Influential scholars beginning with Roscoe Pound and including more recently Professors Redish, Jeffries, Schauer, and Ardia have eroded the prior restraint doctrines’ reasoning and application. The procedure leading to an injunction can be augmented by requiring prior notice, adversary adjudication, and narrow drafting. A properly adjudicated and drafted injunction that specifically forbids defendant’s defamation will prevent harmful torts without threatening free-speech values. The article closes by asking for abolition of the Maxim and suspension or qualification of the prior restraint doctrine for defamation

    Sentiment Analysis of Textual Content in Social Networks. From Hand-Crafted to Deep Learning-Based Models

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    Aquesta tesi proposa diversos mètodes avançats per analitzar automàticament el contingut textual compartit a les xarxes socials i identificar les opinions, emocions i sentiments a diferents nivells d’anàlisi i en diferents idiomes. Comencem proposant un sistema d’anàlisi de sentiments, anomenat SentiRich, basat en un conjunt ric d’atributs, inclosa la informació extreta de lèxics de sentiments i models de word embedding pre-entrenats. A continuació, proposem un sistema basat en Xarxes Neurals Convolucionals i regressors XGboost per resoldre una sèrie de tasques d’anàlisi de sentiments i emocions a Twitter. Aquestes tasques van des de les tasques típiques d’anàlisi de sentiments fins a determinar automàticament la intensitat d’una emoció (com ara alegria, por, ira, etc.) i la intensitat del sentiment dels autors a partir dels seus tweets. També proposem un nou sistema basat en Deep Learning per solucionar el problema de classificació de les emocions múltiples a Twitter. A més, es va considerar el problema de l’anàlisi del sentiment depenent de l’objectiu. Per a aquest propòsit, proposem un sistema basat en Deep Learning que identifica i extreu l'objectiu dels tweets. Tot i que alguns idiomes, com l’anglès, disposen d’una àmplia gamma de recursos per permetre l’anàlisi del sentiment, a la majoria de llenguatges els hi manca. Per tant, utilitzem la tècnica d'anàlisi de sentiments entre idiomes per desenvolupar un sistema nou, multilingüe i basat en Deep Learning per a llenguatges amb pocs recursos lingüístics. Proposem combinar l’ajuda a la presa de decisions multi-criteri i anàlisis de sentiments per desenvolupar un sistema que permeti als usuaris la possibilitat d’explotar tant les opinions com les seves preferències en el procés de classificació d’alternatives. Finalment, vam aplicar els sistemes desenvolupats al camp de la comunicació de les marques de destinació a través de les xarxes socials. Amb aquesta finalitat, hem recollit tweets de persones locals, visitants i els gabinets oficials de Turisme de diferents destinacions turístiques i es van analitzar les opinions i les emocions compartides en ells. En general, els mètodes proposats en aquesta tesi milloren el rendiment dels enfocaments d’última generació i mostren troballes apassionants.Esta tesis propone varios métodos avanzados para analizar automáticamente el contenido textual compartido en las redes sociales e identificar opiniones, emociones y sentimientos, en diferentes niveles de análisis y en diferentes idiomas. Comenzamos proponiendo un sistema de análisis de sentimientos, llamado SentiRich, que está basado en un conjunto rico de características, que incluyen la información extraída de léxicos de sentimientos y modelos de word embedding previamente entrenados. Luego, proponemos un sistema basado en redes neuronales convolucionales y regresores XGboost para resolver una variedad de tareas de análisis de sentimientos y emociones en Twitter. Estas tareas van desde las típicas tareas de análisis de sentimientos hasta la determinación automática de la intensidad de una emoción (como alegría, miedo, ira, etc.) y la intensidad del sentimiento de los autores de los tweets. También proponemos un novedoso sistema basado en Deep Learning para abordar el problema de clasificación de emociones múltiples en Twitter. Además, consideramos el problema del análisis de sentimientos dependiente del objetivo. Para este propósito, proponemos un sistema basado en Deep Learning que identifica y extrae el objetivo de los tweets. Si bien algunos idiomas, como el inglés, tienen una amplia gama de recursos para permitir el análisis de sentimientos, la mayoría de los idiomas carecen de ellos. Por lo tanto, utilizamos la técnica de Análisis de Sentimiento Inter-lingual para desarrollar un sistema novedoso, multilingüe y basado en Deep Learning para los lenguajes con pocos recursos lingüísticos. Proponemos combinar la Ayuda a la Toma de Decisiones Multi-criterio y el análisis de sentimientos para desarrollar un sistema que brinde a los usuarios la capacidad de explotar las opiniones junto con sus preferencias en el proceso de clasificación de alternativas. Finalmente, aplicamos los sistemas desarrollados al campo de la comunicación de las marcas de destino a través de las redes sociales. Con este fin, recopilamos tweets de personas locales, visitantes, y gabinetes oficiales de Turismo de diferentes destinos turísticos y analizamos las opiniones y las emociones compartidas en ellos. En general, los métodos propuestos en esta tesis mejoran el rendimiento de los enfoques de vanguardia y muestran hallazgos interesa.This thesis proposes several advanced methods to automatically analyse textual content shared on social networks and identify people’ opinions, emotions and feelings at a different level of analysis and in different languages. We start by proposing a sentiment analysis system, called SentiRich, based on a set of rich features, including the information extracted from sentiment lexicons and pre-trained word embedding models. Then, we propose an ensemble system based on Convolutional Neural Networks and XGboost regressors to solve an array of sentiment and emotion analysis tasks on Twitter. These tasks range from the typical sentiment analysis tasks, to automatically determining the intensity of an emotion (such as joy, fear, anger, etc.) and the intensity of sentiment (aka valence) of the authors from their tweets. We also propose a novel Deep Learning-based system to address the multiple emotion classification problem on Twitter. Moreover, we considered the problem of target-dependent sentiment analysis. For this purpose, we propose a Deep Learning-based system that identifies and extracts the target of the tweets. While some languages, such as English, have a vast array of resources to enable sentiment analysis, most low-resource languages lack them. So, we utilise the Cross-lingual Sentiment Analysis technique to develop a novel, multi-lingual and Deep Learning-based system for low resource languages. We propose to combine Multi-Criteria Decision Aid and sentiment analysis to develop a system that gives users the ability to exploit reviews alongside their preferences in the process of alternatives ranking. Finally, we applied the developed systems to the field of communication of destination brands through social networks. To this end, we collected tweets of local people, visitors, and official brand destination offices from different tourist destinations and analysed the opinions and the emotions shared in these tweets

    LIPIcs, Volume 277, GIScience 2023, Complete Volume

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    LIPIcs, Volume 277, GIScience 2023, Complete Volum

    A Survey of Social Network Forensics

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    Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent any criminal activities in different forms. It will also help researchers to develop new models / techniques in the future. This paper provides literature review of the social network forensics methods, models, and techniques in order to provide an overview to the researchers for their future works as well as the law enforcement investigators for their investigations when crimes are committed in the cyber space. It also provides awareness and defense methods for OSN users in order to protect them against to social attacks

    Information and Communication Technologies in Tourism 2021

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    This open access book is the proceedings of the International Federation for IT and Travel & Tourism (IFITT)’s 28th Annual International eTourism Conference, which assembles the latest research presented at the ENTER21@yourplace virtual conference January 19–22, 2021. This book advances the current knowledge base of information and communication technologies and tourism in the areas of social media and sharing economy, technology including AI-driven technologies, research related to destination management and innovations, COVID-19 repercussions, and others. Readers will find a wealth of state-of-the-art insights, ideas, and case studies on how information and communication technologies can be applied in travel and tourism as we encounter new opportunities and challenges in an unpredictable world
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