BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Date iCal//NONSGML kigkonsult.se iCalcreator 2.20.2//
METHOD:PUBLISH
X-WR-CALNAME;VALUE=TEXT:Eventi DIAG
BEGIN:VTIMEZONE
TZID:Europe/Paris
BEGIN:STANDARD
DTSTART:20221030T030000
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20220327T020000
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
RDATE:20230326T020000
TZNAME:CEST
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:calendar.24923.field_data.0@oba.diag.uniroma1.it
DTSTAMP:20260408T164729Z
CREATED:20220923T094549Z
DESCRIPTION:AbstractEven though the Internet and social media have increase
 d the amount of news and information people can consume\, most users are o
 nly exposed to content that reinforces their positions and isolates them f
 rom other ideological communities. This environment has real consequences 
 with great impact on our lives like severe political polarization\, easy s
 pread of fake news\, political extremism\, hate groups and the lack of enr
 iching debates\, among others. Therefore\, encouraging conversations betwe
 en different groups of users and  with different points of views is import
 ant for healthy societies.In this talk\, we will focus on how we can use m
 achine learning models with millions of posts from Twitter and Reddit to c
 haracterize how users talk to each other. First\, we will discuss how to a
 pply popular topic modeling algorithms to tweets\, which tend to be shorte
 r and more incoherente than other text corpus\, not only using the text of
  the tweet\, but also the underlying interaction graph. We will show how t
 his methodology could be applied to electoral context datasets in order to
  characterize the interests of users from different political leanings and
 \, in particular\, to community-changing users (people that change from on
 e political community to another one during the campaign).Finally\, we wil
 l analyze the news-sharing behavior of users in Reddit and measure the cau
 sal impact that sharing an article with an opposing political leaning has 
 on the toxicity of the online conversation.  Bio sketchFederico Albanese i
 s a member of the Institute of calculus and PhD student in Computer Scienc
 e at the University of Buenos Aires (UBA). His research focuses on machine
  learning applications on social media. He received his master degree in p
 hysics from the same university. Previously\, he has worked as a data scie
 ntist at Hexagon doing financial analysis and did two internships at Faceb
 ook (now Meta) where he worked on deep bayesian networks and ranking model
 s. He is currently an assistant professor in the master's degree in data m
 ining at the UBA and advisor to two master's students on causal inference 
 of social media messages. *Zoom Link*https://uniroma1.zoom.us/j/8286166075
 9ID riunione: 828 6166 0759  
DTSTART;TZID=Europe/Paris:20220928T140000
DTEND;TZID=Europe/Paris:20220928T140000
LAST-MODIFIED:20220923T101134Z
LOCATION:Aula B203\, Secondo Piano - DIAG Sapienza - Via Ariosto 205
SUMMARY:Machine learning in social media: topic modeling\, community detect
 ion and causal inference - Federico Albanese - University of Buenos Aires
URL;TYPE=URI:http://oba.diag.uniroma1.it/node/24923
END:VEVENT
END:VCALENDAR