  The Web consists of numerous Web communities, news sources, and services,
which are often exploited by various entities for the dissemination of false
information. Yet, we lack tools and techniques to effectively track the
propagation of information across the multiple diverse communities, and to
model the interplay and influence between them. Also, we lack an understanding
of what the role and impact of emerging communities and services on the Web
are, and how such communities are exploited by bad actors that spread false and
weaponized information. In this thesis, we study the information ecosystem on
the Web by presenting a typology that includes the various types of false
information, the involved actors and their possible motives. Then, we follow a
data-driven cross-platform quantitative approach to analyze billions of posts
from Twitter, Reddit, 4chan's /pol/, and Gab, to shed light on: 1) how news and
memes travel from one Web community to another and how we can model and
quantify the influence between Web communities; 2) characterizing the role of
emerging Web communities and services on the Web, by studying Gab and two Web
archiving services, namely the Wayback Machine and archive.is; and 3) how
popular Web communities are exploited by state-sponsored actors for the purpose
of spreading disinformation. Our analysis reveal that fringe Web communities
like 4chan's /pol/ and The_Donald subreddit have a disproportionate influence
on mainstream communities like Twitter with regard to the dissemination of news
and memes. We find that Gab acts as the new hub for the alt-right community,
while for Web archiving services we find that they can be misused to penalize
ad revenue from news sources with conflicting ideology. Finally, when studying
state-sponsored actors, we find that they were particularly influential in
spreading news on popular communities like Twitter and Reddit.
