BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Canada/Eastern BEGIN:DAYLIGHT DTSTART:20220313T030000 TZOFFSETFROM:-0500 TZOFFSETTO:-0400 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:EDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20221106T010000 TZOFFSETFROM:-0400 TZOFFSETTO:-0500 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:EST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20220427T002444Z UID:09795832-0A00-45F9-B291-00352911337F DTSTART;TZID=Canada/Eastern:20220426T180000 DTEND;TZID=Canada/Eastern:20220426T190000 DESCRIPTION:The term "fake news" was pretty much unknown and unpopular a fe w decades ago\, but it has emerged as a massive monster in the digital era of social media. Fake news is spreading like wildfire these days\, and pe ople share it without confirming it. Often\, it is to promote or enforce s pecific views\, and it is carried out through political agendas. Fake news refers to news that may or may not be correct and is widely disseminated via social media and other internet platforms.\n\nIn this digital age\, it is not easy to tackle the spread of fake news\, where thousands of inform ation-sharing sites via fake news or misinformation can be shared. It has become a greater issue as AI advances\, bringing with it artificial bots t hat may be used to create and propagate fake news. The problem is critical because many individuals believe anything they read on the internet\, and those who are inexperienced or new to digital technologies are vulnerable to being misled. Fraud is another issue that can arise as a result of spa m or harmful emails and communications.\n\nFake news has grown in populari ty and spread as a result of recent political events. Humans are inconsist ent\, if not outright terrible detectors of fake news\, as evidenced by th e pervasive effects of the widespread onset of fake news. As a result\, ef forts have been made to automate detecting fake news. The most prominent o f these attempts are "blacklists" of unreliable sources and authors. While these technologies are useful we need to account for more complex instanc es when trusted sources and authors leak fake news in order to provide a c omplete end-to-end solution. As a result\, the goal of this project was to develop a tool that used machine learning and natural language processing techniques to recognize the language patterns that distinguish fake and t rue news. The outcomes of this project show that machine learning can be e ffective in this situation. We developed a model that detects a variety of intuitive indicators of real and fake news and an application to aid in t he visual representation of the classification decision. We aim to give us ers the ability to classify news as fake or real and verify the website le gitimacy that published it.\n\nSpeaker(s): Roshna Babu\, \n\nToronto\, On tario\, Canada\, Virtual: https://events.vtools.ieee.org/m/312334 LOCATION:Toronto\, Ontario\, Canada\, Virtual: https://events.vtools.ieee.o rg/m/312334 ORGANIZER:reza.dibaj@ieee.org) Organizer: Magnetics Chapter, WIE IEEE SEQUENCE:3 SUMMARY:Fake News Detection – Students Research in ML and DL at Durham Co llege URL;VALUE=URI:https://events.vtools.ieee.org/m/312334 X-ALT-DESC:Description: <br /><p>The term "fake news" was pretty much unkno wn and unpopular a few decades ago\, but it has emerged as a massive monst er in the digital era of social media. Fake news is spreading like wildfir e these days\, and people share it without confirming it. Often\, it is to promote or enforce specific views\, and it is carried out through politic al agendas. Fake news refers to news that may or may not be correct and is widely disseminated via social media and other internet platforms.</p>\n< p>In this digital age\, it is not easy to tackle the spread of fake news\, where thousands of information-sharing sites via fake news or misinformat ion can be shared. It has become a greater issue as AI advances\, bringing with it artificial bots that may be used to create and propagate fake new s. The problem is critical because many individuals believe anything they read on the internet\, and those who are inexperienced or new to digital t echnologies are vulnerable to being misled. Fraud is another issue that ca n arise as a result of spam or harmful emails and communications.</p>\n<p> Fake news has grown in popularity and spread as a result of recent politic al events. Humans are inconsistent\, if not outright terrible detectors of fake news\, as evidenced by the pervasive effects of the widespread onset of fake news. As a result\, efforts have been made to automate detecting fake news. The most prominent of these attempts are "blacklists" of unreli able sources and authors. While these technologies are useful we need to a ccount for more complex instances when trusted sources and authors leak fa ke news in order to provide a complete end-to-end solution. As a result\, the goal of this project was to develop a tool that used machine learning and natural language processing techniques to recognize the language patte rns that distinguish fake and true news. The outcomes of this project show that machine learning can be effective in this situation. We developed a model that detects a variety of intuitive indicators of real and fake news and an application to aid in the visual representation of the classificat ion decision. We aim to give users the ability to classify news as fake or real and verify the website legitimacy that published it.</p> END:VEVENT END:VCALENDAR