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<p>Hello</p>
<p>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. <br>
</p>
<div class="moz-forward-container">All the best<br>
Harry<br>
<br>
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<th valign="BASELINE" nowrap="nowrap" align="RIGHT">Subject:
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<td>[WA Section] Understanding Who Spreads Misinformation,
How It Engages, and Why It Works</td>
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<th valign="BASELINE" nowrap="nowrap" align="RIGHT">Date: </th>
<td>Tue, 17 Mar 2026 21:57:05 -0400</td>
</tr>
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<th valign="BASELINE" nowrap="nowrap" align="RIGHT">From: </th>
<td>IEEE eNotice <a class="moz-txt-link-rfc2396E" href="mailto:enotice@enotice.ieee.org"><enotice@enotice.ieee.org></a></td>
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<br>
<br>
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<p
style="line-height: 100%; margin-bottom: 0.21cm; background: #ffffff;"><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;"><strong>Event title</strong></span></span></span><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;">: From Detection to Causation:
Understanding Who Spreads Misinformation, How It
Engages, and Why It Works</span></span></span></p>
<p
style="line-height: 100%; margin-bottom: 0.21cm; background: #ffffff;"><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;"><strong>Organising OU:</strong></span></span></span><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;"> IEEE WA SMC chapter</span></span></span></p>
<p
style="line-height: 100%; margin-bottom: 0.21cm; background: #ffffff;"><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;"><strong>Date and time of the
event</strong></span></span></span><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;">: Thursday, </span></span></span><span
style="color: #212121;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;"> 9 April, 10 – 11 am Perth
time / 12 – 1 pm Sydney time</span></span></span></p>
<p
style="line-height: 100%; margin-bottom: 0.21cm; background: #ffffff;"><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;"><strong>Speaker(s)</strong></span></span></span><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;">: </span></span></span><span
style="color: #212121;"><span
style="font-family: Aptos, sans-serif;"><span
style="font-size: small;">Dr Lin Tian</span></span></span></p>
<p
style="line-height: 100%; margin-bottom: 0.21cm; background: #ffffff;"><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;"><strong>For registration do
this</strong></span></span></span><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;">: </span></span></span><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;">Please email Guanjin Wang (<a
href="mailto:guanjin.wang@murdoch.edu.au?subject=Re: From%20Detection%20to%20Causation:%20Understanding%20Who%20Spreads%20Misinformation,%20How%20It%20Engages,%20and%20Why%20It%20Works"
moz-do-not-send="true">guanjin.wang@murdoch.edu.au</a>)
for RSVP</span></span></span></p>
<p
style="line-height: 100%; margin-bottom: 0.21cm; background: #ffffff;"><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;"><strong>Registration fee, if
any</strong></span></span></span><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;">: N/A</span></span></span></p>
<p
style="line-height: 100%; margin-bottom: 0.21cm; background: #ffffff;"><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;"><strong>Who can attend (IEEE
members and non-members?)</strong></span></span></span><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;">: </span></span></span><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;">IEEE members and non-members</span></span></span></p>
<p
style="line-height: 100%; margin-bottom: 0.21cm; background: #ffffff;"><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;"><strong>Venue or link to
online meeting</strong></span></span></span><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;">: </span></span></span><span
style="color: #0000ff;"><u><a
href="https://enotice.mmsend.com/link.cfm?r=GdyoIEMbwX2_ItrD6mRetg~~&pe=Ye1VmxU_0k_voL2s-v3U10MtqexLbE-MCUPfXJO9qCK-IYoy10La0cEvhdgpBPL1IBrSiMBK6QSQMlKeeNh8eA~~&t=sVdqxO7QrvxNAUG_53Kb6Q~~"
name="Meeting join" moz-do-not-send="true"><span
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<p
style="line-height: 100%; margin-bottom: 0.21cm; background: #ffffff;"> </p>
<p
style="line-height: 100%; margin-bottom: 0.21cm; background: #ffffff;"><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;"><strong>Abstract</strong></span></span></span><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;">:</span></span></span></p>
<p
style="orphans: 2; line-height: 100%; margin-bottom: 0.21cm; widows: 2;"
align="left"><span style="font-family: Calibri, sans-serif;"><span
style="font-size: small;"><span style="color: #212121;"><span
style="font-family: Aptos, sans-serif;">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 </span></span><span style="color: #212121;"><span
style="font-family: Aptos, sans-serif;"><strong>MetaTroll</strong></span></span><span
style="color: #212121;"><span
style="font-family: Aptos, sans-serif;">, 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 </span></span><span
style="color: #212121;"><span
style="font-family: Aptos, sans-serif;"><strong>IC-Mamba</strong></span></span><span
style="color: #212121;"><span
style="font-family: Aptos, sans-serif;">, 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
</span></span><span style="color: #212121;"><span
style="font-family: Aptos, sans-serif;"><em>causal
reasoning</em></span></span><span
style="color: #212121;"><span
style="font-family: Aptos, sans-serif;">, 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 </span></span><span
style="color: #212121;"><span
style="font-family: Aptos, sans-serif;"><strong>Dreams</strong></span></span><span
style="color: #212121;"><span
style="font-family: Aptos, sans-serif;">, 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 </span></span><span
style="color: #212121;"><span
style="font-family: Aptos, sans-serif;"><em>detecting</em></span></span><span
style="color: #212121;"><span
style="font-family: Aptos, sans-serif;"> who spreads
misinformation, to </span></span><span
style="color: #212121;"><span
style="font-family: Aptos, sans-serif;"><em>predicting</em></span></span><span
style="color: #212121;"><span
style="font-family: Aptos, sans-serif;"> how fast it
spreads, to </span></span><span
style="color: #212121;"><span
style="font-family: Aptos, sans-serif;"><em>explaining</em></span></span><span
style="color: #212121;"><span
style="font-family: Aptos, sans-serif;"> why it
engages—offering actionable tools for platforms and
policymakers to intervene earlier and more
effectively.</span></span></span></span></p>
<p
style="line-height: 100%; margin-bottom: 0.21cm; background: #ffffff;"><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;"><strong>Presenter’s Bio:</strong></span></span></span></p>
<p
style="orphans: 2; line-height: 100%; margin-bottom: 0.21cm; widows: 2;"
align="left"><span style="font-family: Calibri, sans-serif;"><span
style="font-size: small;"><span style="color: #212121;"><span
style="font-family: Aptos, sans-serif;">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.</span></span></span></span></p>
<p
style="line-height: 100%; margin-bottom: 0.21cm; background: #ffffff;"><span
style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;"><strong>For further
information contact: </strong></span></span></span><a
href="mailto:Guanjin.Wang@murdoch.edu.au?subject=Re: From%20Detection%20to%20Causation:%20Understanding%20Who%20Spreads%20Misinformation,%20How%20It%20Engages,%20and%20Why%20It%20Works"
moz-do-not-send="true"><span style="color: #1d2228;"><span
style="font-family: Helvetica, sans-serif;"><span
style="font-size: small;">Guanjin.Wang@murdoch.edu.au</span></span></span></a></p>
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