[plug] Fwd: [WA Section] Understanding Who Spreads Misinformation, How It Engages, and Why It Works

[Harry McNally]

Hello

I would not normally cross post events to PLUG but for anyone dealing with 
on-line noise (human or otherwise) I thought this might be interesting.

All the best
Harry

-------- Forwarded Message --------
Subject: 	[WA Section] Understanding Who Spreads Misinformation, How It 
Engages, and Why It Works
Date: 	Tue, 17 Mar 2026 21:57:05 -0400
From: 	IEEE eNotice <enotice at enotice.ieee.org>



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*Event title*: From Detection to Causation: Understanding Who Spreads 
Misinformation, How It Engages, and Why It Works

*Organising OU:*IEEE WA SMC chapter

*Date and time of the event*: Thursday, 9 April, 10 – 11 am Perth time / 12 – 
1 pm Sydney time

*Speaker(s)*: Dr Lin Tian

*For registration do this*: Please email Guanjin Wang 
(guanjin.wang at murdoch.edu.au <mailto:guanjin.wang at murdoch.edu.au?subject=Re: 
From%20Detection%20to%20Causation:%20Understanding%20Who%20Spreads%20Misinformation,%20How%20It%20Engages,%20and%20Why%20It%20Works>) 
for RSVP

*Registration fee, if any*: N/A

*Who can attend (IEEE members and non-members?)*: IEEE members and non-members

*Venue or link to online meeting*: 
_https://teams.microsoft.com/meet/45487336147511?p=wPzRF6KY4xQj5zeWwh 
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*Abstract*:

Social media misinformation and disinformation threaten democratic cohesion 
worldwide, yet our ability to detect, predict, and understand their spread 
remains limited. This talk presents a research line along with tackling this 
challenge across four dimensions. First, I introduce *MetaTroll*, a 
meta-learning framework with campaign-specific transformer adapters that 
detects state-sponsored trolls from novel influence campaigns using only a 
handful of labeled examples, while resisting catastrophic forgetting as new 
campaigns emerge. Second, I present *IC-Mamba*, a state space model that 
forecasts engagement with misinformation within the critical first 15–30 
minutes of posting by modeling interval-censored temporal dynamics—enabling 
early intervention before harmful content goes viral. Third, I move beyond 
prediction to /causal reasoning/, proposing a joint treatment-outcome 
framework that estimates how external attention signals causally drive 
engagement, showing that causal effect measures align more closely with 
expert-assessed influence than follower counts. Finally, I introduce *Dreams*, 
which asks whether neural architectures can discover social exchange 
principles from behavioral data alone, modeling engagement as a 
platform-conditioned negotiation between user effort and social reward across 
seven platforms and 2.37 million posts. Together, these works trace an arc 
from /detecting/ who spreads misinformation, to /predicting/ how fast it 
spreads, to /explaining/ why it engages—offering actionable tools for 
platforms and policymakers to intervene earlier and more effectively.

*Presenter’s Bio:*

Lin Tian is a research fellow at UTS, with strong research interests in AI and 
natural language processing with applications on social media. She holds a PhD 
in Computer Science from RMIT University and specialises in understanding and 
combating misinformation spread across digital platforms. Her work focuses on 
developing explainable, robust deep learning models to detect propaganda, 
coordinated manipulation campaigns, and misinformation on social media. She 
combines causal inference, multi-agent systems, and state-space models to 
trace how information evolves across the web. Her research interests include 
rumour detection, cross-lingual transfer learning, and behavioural 
characterisation of misinformation spreaders. Beyond research, Lin enjoys 
maintaining balance through running and surfing. These activities keep her 
grounded and energised, providing the mental clarity needed for tackling 
complex AI challenges.

*For further information contact: *Guanjin.Wang at murdoch.edu.au 
<mailto:Guanjin.Wang at murdoch.edu.au?subject=Re: 
From%20Detection%20to%20Causation:%20Understanding%20Who%20Spreads%20Misinformation,%20How%20It%20Engages,%20and%20Why%20It%20Works>

Western Australia Section : http://ewh.ieee.org/r10/w_australia/ 
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