Tell Me Why It’s Fake: Developing an Explainable User Interface for a Fake News Detection System

•

0 gefällt mir•11 views

Presentation @ XAI.it 2022: 3rd Italian Workshop on Explainable Artificial Intelligence, co-located with AIxIA 2022, 30th November 2022, Udine, Italy.

Wissenschaft

Tell Me Why It’s Fake:
Developing an Explainable User Interface for
a Fake News Detection System
Erasmo Purificato, Saijal Shahania, and Ernesto William De Luca

“
Disinformation or falsehoods presented as real
news or actual news that is claimed to be untrue.
Fake News definition by the American Dialect Society as 2017 “word of the year”

The need of fake news detection tools
◎ Advent and rapid diffusion of internet bots (Ferrara et al., 2016)
◎ 17% of Twitter accounts are suspected to be bots, contributing to 30% of the
number of tweets posted (Liu, 2019)
◎ 500 million tweets published every day (internetlivestats.com, 2021)

Manual fact-checking tool
(www.politifact.com/truth-o-meter/)

dEFEND(Shu et al., 2019)
Automatic explainable fact-checking tool

Logic architecture of the fake news detection system

Next steps
◎ Implementation of “What if…” functionality;
◎ Addition of new explainability methods;
◎ User study with domain experts to assess the effectiveness of the whole system.

Thanks!
I am Erasmo Purificato
You can find me at:
https://erasmopurif.com
@erasmopurif11
erasmo.purificato@ovgu.de

Weitere ähnliche Inhalte

Ähnlich wie Tell Me Why It’s Fake: Developing an Explainable User Interface for a Fake News Detection System

Student Presentation: AI & Government

ShaynaBlum

Fake News Detection Using Machine Learning

IRJET Journal

Fake news and trust and distrust in fact checking sites

Petter Bae Brandtzæg

Development of a Web Application for Fake News Classification using Machine l...

IRJET Journal

During the last decade, the social media has been regarded as a rich dominant source of information and news. Its unsupervised nature leads to the emergence and spread of fake news. Fake news detection has gained a great importance posing many challenges to the research community. One of the main challenges is the detection accuracy which is highly affected by the chosen and extracted features and the used classification algorithm. In this paper, we propose a context-based solution that relies on account features and random forest classifier to detect fake news. It achieves the precision of 99.8%. The system accuracy has been compared to other commonly used classifiers such as decision tree classifier, Gaussian Naïve Bayes and neural network which give precision of 98.4%, 92.6%, and 62.7% respectively. The experiments’ accuracy results show the possibility of distinguishing fake news and giving credibility scores for social media news with a relatively high performance.

20574-38941-1-PB.pdf

IjictTeam

ap_local_news_ai_report_march_2022.pdf

SGB Media Group

With the spread of social media platforms and the proliferation of misleading news, misinformation detection within microblogging platforms has become a real challenge. During the Covid-19 pandemic, many fake news and rumors were broadcasted and shared daily on social media. In order to filter out these fake news, many works have been done on misinformation detection using machine learning and sentiment analysis in the English language. However, misinformation detection research in the Arabic language on social media is limited. This paper introduces a misinformation verification system for Arabic COVID-19 related news using an Arabic rumors dataset on Twitter. We explored the dataset and prepared it using multiple phases of preprocessing techniques before applying different machine learning classification algorithms combined with a semantic analysis method. The model was applied on 3.6k annotated tweets achieving 93% best overall accuracy of the model in detecting misinformation. We further build another dataset of Covid-19 related claims in Arabic to examine how our model performs with this new set of claims. Results show that the combination of machine learning techniques and linguistic analysis achieves the best scores reaching 92% best accuracy in detecting the veracity of sentences of the new dataset.

Combining Machine Learning and Semantic Analysis for Efficient Misinformation...

AIRCC Publishing Corporation

COMBINING MACHINE LEARNING AND SEMANTIC ANALYSIS FOR EFFICIENT MISINFORMATION...

ijcsit

Journalism, media and technology trends and predictions 2017

Marketing Media Review

IRJET- Detecting Fake News

IRJET Journal

Key Takeaways 2018 marks the beginning of the end of smartphones in the world's largest economies. What's coming next are conversational interfaces with zero-UIs. This will radically change the media landscape, and now is the best time to start thinking through future scenarios. In 2018, a critical mass of emerging technologies will converge finding advanced uses beyond initial testing and applied research. That’s a signal worth paying attention to. News organizations should devote attention to emerging trends in voice interfaces, the decentralization of content, mixed reality, new types of search, and hardware (such as CubeSats and smart cameras). Journalists need to understand what artificial intelligence is, what it is not, and what it means for the future of news. AI research has advanced enough that it is now a core component of our work at FTI. You will see the AI ecosystem represented in many of the trends in this report, and it is vitally important that all decision-makers within news organizations familiarize themselves with the current and emerging AI landscapes. We have included an AI Primer For Journalists in our Trend Report this year to aid in that effort. Decentralization emerged as a key theme for 2018. Among the companies and organizations FTI covers, we discovered a new emphasis on restricted peer-to-peer networks to detect harassment, share resources and connect with sources. There is also a push by some democratic governments around the world to divide internet access and to restrict certain content, effectively creating dozens of “splinternets.” Consolidation is also a key theme for 2018. News brands, broadcast spectrum, and artificial intelligence startups will continue to be merged with and acquired by relatively few corporations. Pending legislation and policy in the U.S., E.U. and in parts of Asia could further concentrate the power among a small cadre of information and technology organizations in the year ahead. To understand the future of news, you must pay attention to the future of many industries and research areas in the coming year. When journalists think about the future, they should broaden the usual scope to consider developments from myriad other fields also participating in the knowledge economy. Technology begets technology. We are witnessing an explosion in slow motion.

[2018] Tech Trends For Journalism and Media – The Future Today Institute

Filipp Paster

Marsden Regulating Disinformation Kluge 342020

Chris Marsden

Akram.pptx

ssuser7f63b0

Artificial Intelligence, elections, media pluralism and media freedom

Centre for Media Pluralism and Media Freedom

IRJET- Fake Message Deduction using Machine Learining

IRJET Journal

UNPROTECTED DATA: REVIEW OF INTERNET ENABLED PSYCHOLOGICAL AND INFORMATION WARFARE Francisco GARCIA MARTINEZ Illinois Institute of Technology, School of Applied Technology, Chicago, Illinois, USA [email protected] Maurice DAWSON Illinois Institute of Technology, School of Applied Technology, Chicago, Illinois, USA [email protected] ABSTRACT Since the last elections in the United States, France, and other nations, fake news has become a tool to manipulate voters. This creation of fake news creates a problem that ripples through an entire society creating division. However, the media has not scrutinized enough on data misuse. Daily it appears that there are breaches causing millions of users to have their personal information taken, exposed, and sold on the Dark Web in exchange of encrypted currencies. Recently, news has surfaced of major social media sites allowing emails to be read without user consent. These issues bring upon concern for the misuse of data and more importantly, how can this be used for information warfare and the exploitation of targeted groups through the use of the Internet. It is essential that organizations continuously review current data policies to ensure that they do not become victims of information warfare. KEYWORDS: data misuse, information warfare, Internet security, intelligence 1. Information Warfare In the battlefield, there is a type of warfare known as psychological operations. This aspect of warfare is used to create a favorable image, gaining adherents, and undermining opponents had already become a significant weapon of 20th-century warfare. However, “they are neither a substitute for power nor a panacea” (Headquarters Department of the Army, 1979, pp. 1-5) but employed correctly they can be instrumental, making the difference between success or failure in military operations. And not exclusively military operations, but also in numerous other fields, such as technology or marketing. Information warfare is, in general terms, a way of protecting one’s information infrastructure while attacking someone else’s by using computers. In the past century, it was commonly considered how future wars would take place and, more importantly, the mean by they would be won (Aldrich, 1996). Consequently, information warfare has become a significant issue in recent decades for both governments and private companies, who have often joined forces to strengthen their economies over their adversaries. For instance, in the United States, government agencies such as the Central Intelligence Agency (CIA), Federal Bureau of Investigation (FBI) or National Security Land Forces Academy Review Vol. XXIV, No 3(95), 2019 DOI: 10.2478/raft-2019-0022 © 2017. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. 187 Bereitgestellt von provisional account | Unauthentifiziert | Heruntergeladen 05.02.20 03:53 UTC A ...

UNPROTECTED DATA REVIEW OF INTERNET ENABLED PSYCHOLOGICAL .docx

ouldparis

Co-Creating Misinformation Resilient Societies

The Open University

Cprs illuminate 2017 ai pr pdf

Jean Valin, APR,FCPRS

45 ways AI is being used in Journalism

Francesco Marconi

Exploring digital fake news phenomenon in indonesia cpr south_short_pdf

Riri Kusumarani

Ähnlich wie Tell Me Why It’s Fake: Developing an Explainable User Interface for a Fake News Detection System (20)

Student Presentation: AI & Government

Fake News Detection Using Machine Learning

Fake news and trust and distrust in fact checking sites

Development of a Web Application for Fake News Classification using Machine l...

20574-38941-1-PB.pdf

ap_local_news_ai_report_march_2022.pdf

Combining Machine Learning and Semantic Analysis for Efficient Misinformation...

COMBINING MACHINE LEARNING AND SEMANTIC ANALYSIS FOR EFFICIENT MISINFORMATION...

Journalism, media and technology trends and predictions 2017

IRJET- Detecting Fake News

[2018] Tech Trends For Journalism and Media – The Future Today Institute

Marsden Regulating Disinformation Kluge 342020

Akram.pptx

Artificial Intelligence, elections, media pluralism and media freedom

IRJET- Fake Message Deduction using Machine Learining

UNPROTECTED DATA REVIEW OF INTERNET ENABLED PSYCHOLOGICAL .docx

Co-Creating Misinformation Resilient Societies

Cprs illuminate 2017 ai pr pdf

45 ways AI is being used in Journalism

Exploring digital fake news phenomenon in indonesia cpr south_short_pdf

Mehr von Erasmo Purificato

Recent Advances in Fairness Analysis of User Profiling Approaches in E-Commer...

Erasmo Purificato

Leveraging Graph Neural Networks for User Profiling: Recent Advances and Open...

Erasmo Purificato

Tutorial on User Profiling with Graph Neural Networks and Related Beyond-Acc...

Erasmo Purificato

Do Graph Neural Networks Build Fair User Models? Assessing Disparate Impact a...

Erasmo Purificato

Beyond-Accuracy Perspectives: Explainability and Fairness

Erasmo Purificato

Evaluating Explainable Interfaces for a Knowledge Graph-Based Recommender System

Erasmo Purificato

Beyond-Accuracy Perspectives on Graph Neural Network-Based Models for Behavio...

Erasmo Purificato

Mehr von Erasmo Purificato (7)

Recent Advances in Fairness Analysis of User Profiling Approaches in E-Commer...

Leveraging Graph Neural Networks for User Profiling: Recent Advances and Open...

Tutorial on User Profiling with Graph Neural Networks and Related Beyond-Acc...

Do Graph Neural Networks Build Fair User Models? Assessing Disparate Impact a...

Beyond-Accuracy Perspectives: Explainability and Fairness

Evaluating Explainable Interfaces for a Knowledge Graph-Based Recommender System

Beyond-Accuracy Perspectives on Graph Neural Network-Based Models for Behavio...

Kürzlich hochgeladen

Human genetics..........................pptx

Silpa

CURRENT SCENARIO OF POULTRY PRODUCTION IN INDIA

Dr. TATHAGAT KHOBRAGADE

Introduction of DNA analysis in Forensic's .pptx

rohankumarsinghrore1

Bacterial Identification and Classifications

Areesha Ahmad

Site Acceptance Test .

Poonam Aher Patil

FAIRSpectra - Enabling the FAIRification of Spectroscopy and Spectrometry

Alex Henderson

PSYCHOSOCIAL NEEDS. in nursing II sem pptx

Suji236384

Conjugation, transduction and transformation

Areesha Ahmad

Clean In Place(CIP).pptx .

Poonam Aher Patil

Bhiwandi Bhiwandi ❤CALL GIRL 7870993772 ❤CALL GIRLS ESCORT SERVICE In Bhiwan...

Monika Rani

Grade 7 - Lesson 1 - Microscope and Its Functions

OrtegaSyrineMay

Context. WASP-76 b has been a recurrent subject of study since the detection of a signature in high-resolution transit spectroscopy data indicating an asymmetry between the two limbs of the planet. The existence of this asymmetric signature has been confirmed by multiple studies, but its physical origin is still under debate. In addition, it contrasts with the absence of asymmetry reported in the infrared (IR) phase curve. Aims. We provide a more comprehensive dataset of WASP-76 b with the goal of drawing a complete view of the physical processes at work in this atmosphere. In particular, we attempt to reconcile visible high-resolution transit spectroscopy data and IR broadband phase curves. Methods. We gathered 3 phase curves, 20 occultations, and 6 transits for WASP-76 b in the visible with the CHEOPS space telescope. We also report the analysis of three unpublished sectors observed by the TESS space telescope (also in the visible), which represents 34 phase curves. Results. WASP-76 b displays an occultation of 260±11 and 152±10 ppm in TESS and CHEOPS bandpasses respectively. Depending on the composition assumed for the atmosphere and the data reduction used for the IR data, we derived geometric albedo estimates that range from 0.05 ± 0.023 to 0.146 ± 0.013 and from <0.13 to 0.189 ± 0.017 in the CHEOPS and TESS bandpasses, respectively. As expected from the IR phase curves, a low-order model of the phase curves does not yield any detectable asymmetry in the visible either. However, an empirical model allowing for sharper phase curve variations offers a hint of a flux excess before the occultation, with an amplitude of ∼40 ppm, an orbital offset of ∼−30◦ , and a width of ∼20◦ . We also constrained the orbital eccentricity of WASP-76 b to a value lower than 0.0067, with a 99.7% confidence level. This result contradicts earlier proposed scenarios aimed at explaining the asymmetry observed in high-resolution transit spectroscopy. Conclusions. In light of these findings, we hypothesise that WASP-76 b could have night-side clouds that extend predominantly towards its eastern limb. At this limb, the clouds would be associated with spherical droplets or spherically shaped aerosols of an unknown species, which would be responsible for a glory effect in the visible phase curves.

Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b

Sérgio Sacani

biology HL practice questions IB BIOLOGY

1301aanya

An introduction on sequence tagged site mapping

adibshanto115

GBSN - Microbiology (Unit 1)

Areesha Ahmad

POGONATUM : morphology, anatomy, reproduction etc.

Silpa

FAIRSpectra - Enabling the FAIRification of Analytical Science

Alex Henderson

PATNA CALL GIRLS 8617370543 LOW PRICE ESCORT SERVICE low price Call me 8617370543 100%genuine sexy VIP call girl safe service WhatsApp chat. 8617370543 Normal call kijiye 8617370543 100% genuine young college girl and housewife Full enjoy open minded girl provide 24hour full cooperative model and full satisfactions☎️*8617370543*⭐Escorts service █▬█⓿▀█▀ call girls and bhabhi available for room Sex and video call service A-1 HIGH CLASS CALL GIRLS TOP MODEL 24X7 HOME/HOTEL Call Girls Safe & Secure High Class Sm Affordable Rate 100% Satisfaction, Unlimited Enjoyment. Any Time for Model/Escort in High class luxury and premium

PATNA CALL GIRLS 8617370543 LOW PRICE ESCORT SERVICE

Goa Call Girls High Profile Escorts

GBSN - Microbiology (Unit 2)

Areesha Ahmad

module for grade 9 for distance learning

levieagacer

Kürzlich hochgeladen (20)

Human genetics..........................pptx

CURRENT SCENARIO OF POULTRY PRODUCTION IN INDIA

Introduction of DNA analysis in Forensic's .pptx

Bacterial Identification and Classifications

Site Acceptance Test .

FAIRSpectra - Enabling the FAIRification of Spectroscopy and Spectrometry

PSYCHOSOCIAL NEEDS. in nursing II sem pptx

Conjugation, transduction and transformation

Clean In Place(CIP).pptx .

Bhiwandi Bhiwandi ❤CALL GIRL 7870993772 ❤CALL GIRLS ESCORT SERVICE In Bhiwan...

Grade 7 - Lesson 1 - Microscope and Its Functions

Asymmetry in the atmosphere of the ultra-hot Jupiter WASP-76 b

biology HL practice questions IB BIOLOGY

An introduction on sequence tagged site mapping

GBSN - Microbiology (Unit 1)

POGONATUM : morphology, anatomy, reproduction etc.

FAIRSpectra - Enabling the FAIRification of Analytical Science

PATNA CALL GIRLS 8617370543 LOW PRICE ESCORT SERVICE

GBSN - Microbiology (Unit 2)

module for grade 9 for distance learning

Tell Me Why It’s Fake: Developing an Explainable User Interface for a Fake News Detection System

1. Tell Me Why It’s Fake: Developing an Explainable User Interface for a Fake News Detection System Erasmo Purificato, Saijal Shahania, and Ernesto William De Luca

2. The concept of Fake News

3. “ Disinformation or falsehoods presented as real news or actual news that is claimed to be untrue. Fake News definition by the American Dialect Society as 2017 “word of the year”

4. The need of fake news detection tools ◎ Advent and rapid diffusion of internet bots (Ferrara et al., 2016) ◎ 17% of Twitter accounts are suspected to be bots, contributing to 30% of the number of tweets posted (Liu, 2019) ◎ 500 million tweets published every day (internetlivestats.com, 2021)

5. Manual fact-checking tool (www.politifact.com/truth-o-meter/)

6. dEFEND(Shu et al., 2019) Automatic explainable fact-checking tool

7. Logic architecture of the fake news detection system

8. Input methods

9. Explainable User Interface

10. Explainable User Interface

11. Explainable User Interface

12. Explainable User Interface

13. Explainer Dashboard

14. Next steps ◎ Implementation of “What if…” functionality; ◎ Addition of new explainability methods; ◎ User study with domain experts to assess the effectiveness of the whole system.

15. Thanks! I am Erasmo Purificato You can find me at: https://erasmopurif.com @erasmopurif11 erasmo.purificato@ovgu.de

Tell Me Why It’s Fake: Developing an Explainable User Interface for a Fake News Detection System

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Ähnlich wie Tell Me Why It’s Fake: Developing an Explainable User Interface for a Fake News Detection System

Ähnlich wie Tell Me Why It’s Fake: Developing an Explainable User Interface for a Fake News Detection System (20)

Mehr von Erasmo Purificato

Mehr von Erasmo Purificato (7)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

Tell Me Why It’s Fake: Developing an Explainable User Interface for a Fake News Detection System