4,768 research outputs found
Misinformation Detection in Social Media
abstract: The pervasive use of social media gives it a crucial role in helping the public perceive reliable information. Meanwhile, the openness and timeliness of social networking sites also allow for the rapid creation and dissemination of misinformation. It becomes increasingly difficult for online users to find accurate and trustworthy information. As witnessed in recent incidents of misinformation, it escalates quickly and can impact social media users with undesirable consequences and wreak havoc instantaneously. Different from some existing research in psychology and social sciences about misinformation, social media platforms pose unprecedented challenges for misinformation detection. First, intentional spreaders of misinformation will actively disguise themselves. Second, content of misinformation may be manipulated to avoid being detected, while abundant contextual information may play a vital role in detecting it. Third, not only accuracy, earliness of a detection method is also important in containing misinformation from being viral. Fourth, social media platforms have been used as a fundamental data source for various disciplines, and these research may have been conducted in the presence of misinformation. To tackle the challenges, we focus on developing machine learning algorithms that are robust to adversarial manipulation and data scarcity.
The main objective of this dissertation is to provide a systematic study of misinformation detection in social media. To tackle the challenges of adversarial attacks, I propose adaptive detection algorithms to deal with the active manipulations of misinformation spreaders via content and networks. To facilitate content-based approaches, I analyze the contextual data of misinformation and propose to incorporate the specific contextual patterns of misinformation into a principled detection framework. Considering its rapidly growing nature, I study how misinformation can be detected at an early stage. In particular, I focus on the challenge of data scarcity and propose a novel framework to enable historical data to be utilized for emerging incidents that are seemingly irrelevant. With misinformation being viral, applications that rely on social media data face the challenge of corrupted data. To this end, I present robust statistical relational learning and personalization algorithms to minimize the negative effect of misinformation.Dissertation/ThesisDoctoral Dissertation Computer Science 201
World Environment Day: Understanding Environmental Programs Impact on Society Using Twitter Data Mining
Environmental sustainability awareness has encouraged the promotion of a number of environmental programs and initiatives and, accordingly, the use of social networks for the dissemination and support of these initiatives has grown significantly. Thus, the purpose of the work is to understand United Nations World Environment Day (WED) programs impact on the digital public debate using Twitter data mining. For that, an ad hoc methodology is designed to provide it to authorities and organizations that wish to analyze the impact of different initiatives or programs on society. All in all, the research carried out analyzes more than 400,000 tweets sent during the 2021 edition of the WED. The tweets have been processed using Big Data techniques and Social Network Analysis. The research reveals that the WED was a trending topic initiative that was discussed in positive terms, where collective sentiment was shown. The topics covered dealt with the event day and the different initiatives related to restoration of ecosystems. However, it is noted that: there is no coordinated action by the institutions, groups or individuals involved in the conversation and the initiative tends towards homophily; digital mobilization is mostly centered in the host country (Pakistan) and, above all, in the neighboring country (India) and, the conspicuous absence of the business sphere in the discussion.Open Access funding provided thanks to the CRUEâCSIC agreement with Springer Nature
Global Innovations in Measurement and Evaluation
We researched the latest developments in theory and practice in measurement and evaluation. And we found that new thinking, techniques, and technology are influencing and improving practice. This report highlights 8 developments that we think have the greatest potential to improve evaluation and programme design, and the careful collection and use of data. In it, we seek to inform and inspireâto celebrate what is possible, and encourage wider application of these ideas
COMPRESSIVE SENSING-BASED METHODOLOGIES FOR SMART GRID MONITORING
Modern distribution networks, commonly known as Smart Grids, will be characterized by strictly requirements in terms of reliability and efficiency of the power supply. This will require a high empowerment in the management of the distribution, and transmission, networks by the system operators.
Problems such as the identification of the prevailing harmonic sources and the fault location are characterized by criticality which must be appropriately taken into account, in order to fully exploit the capabilities of the Smart Grids.
The analysis of both phenomena requires an appropriate monitoring of the networks, which are currently characterized by the availability of a limited number of measurements. This increase the complexity of the analysis of distribution networks, and the necessity of developing ad-hoc algorithms and solutions aimed at supporting the system operators while managing the networks.
In this thesis, Compressive Sensing-based algorithms for detecting the main harmonic polluting sources, and for identifying the location of faults occurring in distribution systems have been presented.
With reference to the identification of the main harmonic sources, two algorithms have been proposed: one for detailed analysis, with reference to a specific harmonic order, and one for more general analysis, which allows to investigate multiple harmonic orders simultaneously. The performed tests have proved how both methodologies are robust with respect to the measurement uncertainties, underlying the different capabilities of the two methods.
Contrarily, the performance of the fault location algorithms are more influenced by the higher uncertainties in measuring the dynamic signals involved during the fault. The analysis performed considering the proper uncertainty scenarios have underlined how the use of modern devices for branch current measurements allow to increase the performance of the fault location algorithms; providing additional information which are useful for locating the fault
The nexus between COVID-19 deaths, air pollution and economic growth in New York state : Evidence from Deep Machine Learning
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BlogForever: D2.5 Weblog Spam Filtering Report and Associated Methodology
This report is written as a first attempt to define the BlogForever spam detection strategy. It comprises a survey of weblog spam technology and approaches to their detection. While the report was written to help identify possible approaches to spam detection as a component within the BlogForver software, the discussion has been extended to include observations related to the historical, social and practical value of spam, and proposals of other ways of dealing with spam within the repository without necessarily removing them. It contains a general overview of spam types, ready-made anti-spam APIs available for weblogs, possible methods that have been suggested for preventing the introduction of spam into a blog, and research related to spam focusing on those that appear in the weblog context, concluding in a proposal for a spam detection workflow that might form the basis for the spam detection component of the BlogForever software
Computer Vision System for Non-Destructive and Contactless Evaluation of Quality Traits in Fresh Rocket Leaves (Diplotaxis Tenuifolia L.)
La tesi di dottorato Ăš incentrata sull'analisi di tecnologie non distruttive per il controllo della
qualitĂ dei prodotti agroalimentari, lungo l'intera filiera agroalimentare. In particolare, la tesi
riguarda l'applicazione del sistema di visione artificiale per valutare la qualitĂ delle foglie di
rucola fresh-cut. La tesi Ăš strutturata in tre parti (introduzione, applicazioni sperimentali e
conclusioni) e in cinque capitoli, rispettivamente il primo e il secondo incentrati sulle
tecnologie non distruttive e in particolare sui sistemi di computer vision per il monitoraggio
della qualitĂ dei prodotti agroalimentari. Il terzo, quarto e quinto capitolo mirano a valutare le
foglie di rucola sulla base della stima di parametri qualitativi, considerando diversi aspetti: (i)
la variabilitĂ dovuta alle diverse pratiche agricole, (ii) la senescenza dei prodotti confezionati
e non, e (iii) lo sviluppo e sfruttamento dei vantaggi di nuovi modelli piĂč semplici rispetto al
machine learning utilizzato negli esperimenti precedenti. Il lavoro di ricerca di questa tesi di
dottorato Ăš stato svolto dall'UniversitĂ di Foggia, dall'Istituto di Scienze delle Produzioni
Alimentari (ISPA) e dall'Istituto di Tecnologie e Sistemi Industriali Intelligenti per le
Manifatture Avanzate (STIIMA) del Consiglio Nazionale delle Ricerche (CNR). LâattivitĂ di
ricerca Ăš stata condotta nell'ambito del Progetto SUS&LOW (Sustaining Low-impact Practices
in Horticulture through Non-destructive Approach to Provide More Information on Fresh
Produce History & Quality), finanziato dal MUR-PRIN 2017, e volto a sostenere la qualitĂ
della produzione e dell'ambiente utilizzando pratiche agricole a basso input e la valutazione
non distruttiva della qualitĂ di prodotti ortofrutticoli.The doctoral thesis focused on the analysis of non-destructive technologies available for the
control quality of agri-food products, along the whole supply chain. In particular, the thesis
concerns the application of computer vision system to evaluate the quality of fresh rocket
leaves. The thesis is structured in three parts (introduction, experimental applications and
conclusions) and in 5 chapters, the first and second focused on non-destructive technologies
and in particular on computer vision systems for monitoring the quality of agri-food products,
respectively. The third, quarter, and fifth chapters aim to assess the rocket leaves based on the
estimation of quality aspects, considering different aspects: (i) the variability due to the
different agricultural practices, (ii) the senescence of packed and unpacked products, and (iii)
development and exploitation of the advantages of new models simpler than the machine
learning used in the previous experiments. The research work of this doctoral thesis was carried
out by the University of Foggia, the Institute of Science of Food Production (ISPA) and the
Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing
(STIIMA) of National Research Council (CNR). It was conducted within the Project
SUS&LOW (Sustaining Low-impact Practices in Horticulture through Non-destructive
Approach to Provide More Information on Fresh Produce History & Quality), funded by MUR-
PRIN 2017, and aimed at sustaining quality of production and of the environment using low
input agricultural practices and non-destructive quality evaluation
Trends and Topics: Characterizing Echo Chambers' Topological Stability and In-group Attitudes
Social Network sites are fertile ground for several polluting phenomena
affecting online and offline spaces. Among these phenomena are included echo
chambers, closed systems in which the opinions expressed by the people inside
are exacerbated for the effect of the repetition, while opposite views are
actively excluded. This paper offers a framework to explore, in a
platform-independent manner, the topological changes through time of echo
chambers, while considering the content posted by users and the attitude
conveyed in discussing specific controversial issues.
The proposed framework consists of four steps: (i) data collection and
annotation of users' ideology regarding a controversial topic, (ii)
construction of a dynamic network of interactions, (iii) ECs extraction and
analysis of their dynamics, and (iv) topic extraction and valence analysis. The
paper then enhances the formalization of the framework by conducting a case
study on Reddit threads about sociopolitical issues (gun control, American
politics, and minorities discrimination) during the first two years and a half
of Donald Trump's presidency.
The results unveil that users often stay inside echo chambers over time.
Furthermore, in the analyzed discussions, the focus is on controversies related
to right-wing parties and specific events in American and Canadian politics.
The analysis of the attitude conveyed in the discussions shows a slight
inclination toward a more negative or neutral attitude when discussing
particularly sensitive issues, such as fascism, school shootings, or police
violence.Comment: 24 pages, including 8 pages of Supplementary materials. Submitted to
IEEE Acces
The future of Cybersecurity in Italy: Strategic focus area
This volume has been created as a continuation of the previous one, with the aim of outlining a set of focus areas and actions that the Italian Nation research community considers essential. The book touches many aspects of cyber security, ranging from the definition of the infrastructure and controls needed to organize cyberdefence to the actions and technologies to be developed to be better protected, from the identification of the main technologies to be defended to the proposal of a set of horizontal actions for training, awareness raising, and risk management
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