KnOD 2021

Beyond Facts: Online Discourse and Knowledge Graphs
A preface to the proceedings of the 1st International Workshop on Knowledge Graphs for Online Discourse Analysis (KnOD 2021, co-located with TheWebConf’21)

https://ceur-ws.org/Vol-2877/preface.pdf

https://knod2021.wordpress.com/
 
ABSTRACT
Expressing opinions and interacting with others on the Web has led to the production of an abundance of online discourse data, such as claims and viewpoints on controversial topics, their sources and contexts. This data constitutes a valuable source of insights for studies into misinformation spread, bias reinforcement, echo chambers or political agenda setting. While knowledge graphs promise to provide the key to a Web of structured information, they are mainly focused on facts without keeping track of the diversity, connection or temporal evolution of online discourse data. As opposed to facts, claims are inherently more complex. Their interpretation strongly depends on the context and a variety of intentional or unintended meanings, where terminology and conceptual understandings strongly diverge across communities from computational social science, to argumentation mining, fact-checking, or view-point/stance detection.

With the Web evolving into a ubiquitous platform giving the opportunity to everyone to publish content, express opinions and interact with others, understanding online discourse has become an increasingly important issue. We define online discourse as any kind of narrative, debate or conversation that happens on the Web, including social networks or news media, involving claims and stances on controversial topics, their sources and contexts (such as related events or entities).

One crucial requirement to facilitate the aforementioned re search areas is the availability of reliable structured knowledge about key notions such as claims, truth ratings, evidence, sources, arguments and their relations. On the one hand, initiatives such as the schema.org Claim Review vocabulary aim at encouraging web site providers to offer such date through embedded Web markup. On the other hand, initial knowledge graphs (KG) such as Mul tiFC [2], ClaimsKG [10], TweetsCOV19 [5] or TweetsKB [6] have been proposed aimed at consolidating Web-mined data about the aforementioned notions. While knowledge graphs (KGs) promise to provide the key to a Web of structured information, they are mainly focused on facts without keeping track of the diversity, connection or temporal evolution of online discourse. 

Daniel Schwabe proposed his take on trust and information disorders seen as disputes of narratives -> http://users.ics.forth.gr/~fafalios/KnOD2021/KNoD2021_Daniel_Schwabe_Keynote.pdf

Misinformation X Disinformation

Emily K. Vraga & Leticia Bode (2020) Defining Misinformation and Understanding its Bounded Nature: Using Expertise and
Evidence for Describing Misinformation, Political Communication, 37:1, 136-144, DOI: 10.1080/10584609.2020.1716500

Shades of misinformation: Controversial [base da pirâmide]

Consuming information
• It’s only relevant when connected to some action – now or in the future

Issues in countering information disorders
• It’s not about truth!
• A dispute of narratives

Daniel Schwabe. 2019. Trust and Privacy in Knowledge Graphs. In Companion Proceedings of WWW '19. ACM, New York, NY, USA, 722–728.
DOI:https://doi.org/10.1145/3308560.3317705

What is trust (in information)? Trust is knowledge-based reliance on received information. (Gerck)
• For our purposes, trust = belief
• Trust as a social process.
• Some properties of trust
• It only matters when some action is involved (Castelfranchi’s delegation)
• It is held towards information provided by some agent regarding some “matter”.
• It is binary! You can’t “half trust”, because you can’t “half act”

If KGs provide “ground truth”, how do we control quality?
• KGs must represent “points of view” explicitly!

ClaimsKG -> https://data.gesis.org/claimskg/site/