# RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection
Using In-Context Learning
based on Emotional Information

Zhiwei Liu1 Kailai Yang1 Qianqian Xie2 Christine de Kock3
Sophia Ananiadou1,4,5 Eduard Hovy3,6
1 The University of Manchester 2 The Fin AI 3 The University of Melbourne
4 Artificial Intelligence Research Center 5 Archimedes/Athena RC 6 Carnegie
Mellon University
<EMAIL_ADDRESS>
<EMAIL_ADDRESS><EMAIL_ADDRESS>
Work done during Zhiwei’s internship at the University of Melbourne

###### Abstract

Misinformation is prevalent in various fields such as education, politics,
health, etc., causing significant harm to society. However, current methods
for cross-domain misinformation detection rely on time and resources consuming
fine-tuning and complex model structures. With the outstanding performance of
LLMs, many studies have employed them for misinformation detection.
Unfortunately, they focus on in-domain tasks and do not incorporate
significant sentiment and emotion features (which we jointly call affect). In
this paper, we propose RAEmoLLM, the first retrieval augmented (RAG) LLMs
framework to address cross-domain misinformation detection using in-context
learning based on affective information. It accomplishes this by applying an
emotion-aware LLM to construct a retrieval database of affective embeddings.
This database is used by our retrieval module to obtain source-domain samples,
which are subsequently used for the inference module’s in-context few-shot
learning to detect target domain misinformation. We evaluate our framework on
three misinformation benchmarks. Results show that RAEmoLLM achieves
significant improvements compared to the zero-shot method on three datasets,
with the highest increases of 20.69%, 23.94%, and 39.11% respectively. This
work will be released on https://github.com/lzw108/RAEmoLLM.

RAEmoLLM: Retrieval Augmented LLMs for Cross-Domain Misinformation Detection
Using In-Context Learning
based on Emotional Information

Zhiwei Liu1††thanks: Work done during Zhiwei’s internship at the University of
Melbourne Kailai Yang1 Qianqian Xie2 Christine de Kock3 Sophia Ananiadou1,4,5
Eduard Hovy3,6 1 The University of Manchester 2 The Fin AI 3 The University of
Melbourne 4 Artificial Intelligence Research Center 5 Archimedes/Athena RC 6
Carnegie Mellon University
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>

##  1 Introduction

The internet is flooded with misinformation Scheufele and Krause (2019), which
has a significant impact on people’s lives and societal stability Della
Giustina (2023). Misinformation is pervasive across various domains such as
education, health, technology, and especially on the internet, which requires
people to invest significant time and effort in discerning the truth Pérez-
Rosas et al. (2018). However, models trained in specific known domains are
often fragile and prone to making incorrect predictions when presented with
samples from new domains Saikh et al. (2020). As a result, detecting cross-
domain misinformation has become an urgent global issue and poses greater
challenges and difficulties.

Although some studies address cross-domain misinformation detection Comito et
al. (2023); Tang et al. (2023); Shi et al. (2023), they require time-consuming
fine-tuning, and apply only traditional machine learning methods or complex
deep learning methods. Recently, LLMs have achieved impressive results in
various tasks through zero-shot, few-shot Li (2023), or instruction tuning
Zhang et al. (2023a). Many researchers have applied LLMs to identify
misinformation Li et al. (2023); Hu et al. (2024); Cheung and Lam (2023).
However, these methods perform only in-domain misinformation detection.
Moreover, emotions and sentiments (which we jointly call affect) are important
characteristics of human expression and communication Hakak et al. (2017).
When authors publish misinformation, they often consciously choose specific
emotions to capture the attention and resonance of readers to encourage rapid
spread Keen (2006); Liu et al. (2024d). Unfortunately, there are few LLMs that
utilize affective information to detect misinformation, and the only
ConspEmoLLM Liu et al. (2024b) are developed based on an emotional LLM, which
does not make full use of affective information, has no cross-domain ability,
and also needs time-consuming fine-tuning.

In-context learning (ICL) needs only task instructions and few-shot examples
(input-label pairs), eliminating fine-tuning on specific task labels Dong et
al. (2022b). A few studies have used ICL to address cross-domain problems Long
et al. (2023); Wu et al. (2024). To the best of our knowledge, there is
currently no application of ICL for cross-domain misinformation detection
based on affective information retrieval.

To address these issues, we propose the first retrieval augmented (RAG) LLMs
framework based on emotional information (RAEmoLLM), to address cross-domain
misinformation detection using in-context learning based on affective
information. RAEmoLLM contains three modules: (1) In the index construction
module, we apply EmoLLaMA-chat-7B Liu et al. (2024c) to encode all domain
corpora, obtaining implicit embeddings to construct the retrieval database as
well as explicit affective labels. We also conduct a comprehensive affective
analysis to demonstrate the effectiveness of affective information for
discriminating between true and misinformation. (2) The retrieval module
recommends the top K affect-related examples (text-label pairs) from the
source domain corpus according to the target domain content, obtained from the
retrieval database. (3) These examples are utilized as the few-shot
demonstrations in the inference module, which is driven by a prompt template
to guide the LLM to verify the target content for misinformation. The template
helps combine implicit and explicit affective information.

In this work, we make three main contributions:

  * •

We conduct affective analysis on different kinds of misinformation datasets
and construct the retrieval database according to the implicit affective
information for misinformation datasets.

  * •

We propose RAEmoLLM, the first framework for cross-domain misinformation
detection using ICL based on affective information, which does not require
fine-tuning. Experimental results show that RAEmoLLM outperforms the zero-shot
methods and the few-shot methods without using affective information.

  * •

We evaluate RAEmoLLM on a variety of misinformation benchmarks, including fake
news, rumors, and conspiracy theory datasets. Results show that RAEmoLLM
achieves significant improvements compared to the zero-shot method on three
datasets, with the highest increases of 20.69%, 23.94%, and 39.11%
respectively, which illustrate the effectiveness of RAEmoLLM framework.

##  2 Related Work

###  2.1 Misinformation detection

Cross-domain misinformation detection: Comito et al. (2023) propose a deep
learning-based architecture able to mitigate this problem by yielding high-
level cross-domain features. Tang et al. (2023) design the News Optimal
Transport to learn transferable features across domains by aligning the source
and target news using Optimal Transport (OT) techniques. Shi et al. (2023)
develop a rough-fuzzy graph learning framework that uses representations of
cross-domain sample uncertainty structural information, and captures shared
general features across domains. But these methods all require complex
structures and fine-tuning strategies.

Based on affective information: Emotion and sentiment are important features
for misinformation detection Liu et al. (2024d). Zhang et al. (2023b) combine
the use of semantic and sentiment information, along with propagation
information for rumor detection. Dong et al. (2022a) design a sentiment-aware
hyper-graph attention network for fake news detection. Liu et al. (2024b)
develop a conspiracy theory detection LLM by fine-tuning EmoLLaMA Liu et al.
(2024c). Choudhry et al. (2022) utilize emotional information for fake news
detection based on an adversarial learning structure. Unfortunately, these
works either have complex structural designs or fine-tuned models, which
require significant time and computational resources. The RAEmoLLM in this
article applies the ICL method based on affective information, which has a
simple structure and does not involve fine-tuning.

Figure 1: The architecture of RAEmoLLM. D: Domain. T: Target domain. S: Source
domain. C: Corpus. L: Label. Aff: Affective information.

###  2.2 In-context learning

Liu et al. (2024a) develop in-context curriculum learning, a simple but
helpful demonstration ordering method for ICL that gradually increases the
complexity of prompt demonstrations. Xu and Zhang (2024) propose in-context
reflection to strategically select demonstrations that reduce the discrepancy
between the LLM’s outputs and the actual input-output mappings. Long et al.
(2023) propose a retrieval-enhanced language model to address cross-domain
problems, in which they train language models by learning both target domain
distribution and the discriminative task signal simultaneously with the
augmented cross-domain in-context examples. Inspired by this work, we propose
the RAEmoLLM.

##  3 Methodology

This section introduces our method of cross-domain misinformation detection,
using the index construction module, retrieval module and inference module.
The overall architecture of RAEmoLLM is shown in Figure 1. In the index
construction module we collect domain datasets, and employ an emotional LLM to
obtain embeddings as well as affective labels to conduct a comprehensive
affective analysis on them to detect the affective differences between real
and false information. The implicit embeddings are adopted to construct the
retrieval database. This database is used by the retrieval module to obtain
source-domain examples. These results are the few-shot examples used by the
inference module’s in-context learning to detect target domain misinformation.

###  3.1 Index Construction Module

####  3.1.1 Datasets

Table 1:  Statistic of datasets

AMTCele | PHEME | COCO
---|---|---
Domain | Legit | Fake | Events | Rumours | Non-rumours | Topics | Related | Conspiracy
Technology | 40 | 40 | Charlie Hebdo | 458 | 1621 | Fake Virus | 248 | 612
Education | 40 | 40 | Sydney siege | 522 | 699 | Harmful Radiation
Business | 40 | 40 | Ferguson | 284 | 859 | Depopulation
Sports | 40 | 40 | Ottawa shooting | 470 | 420 | Other 9 domains | 540 | 1181
Politics | 40 | 40 | Germanwings-crash | 238 | 231 | Total | 788 | 1793
Entertainment | 40 | 40 | Putin missing | 126 | 112 |  |  |
Celebrities | 250 | 250 | Prince Toronto | 229 | 4 |  |  |
Total | 490 | 490 | Gurlitt | 61 | 77 |  |  |
|  |  | Ebola Essien | 14 | 0 |  |  |
|  |  | Total | 2402 | 4023 |  |  |

Table 2:  Statistics values of EIreg and Vreg on different datasets. The t-test is conducted between legit/non-rumours/related and fake/rumours/conspiracy. Datasets | Affective | sub-emotion | legit/non-rumours/related | fake/rumours/conspiracy | t-test
---|---|---|---|---|---
mean | var | mean | var | t | p
AMTCele | EIreg | Anger | 0.3584 | 0.0064 | 0.4055 | 0.0060 | -9.3294 | 6.91E-20
Fear | 0.3587 | 0.0137 | 0.4047 | 0.0124 | -6.2861 | 4.90E-10
Joy | 0.3392 | 0.0180 | 0.2897 | 0.0142 | 6.1054 | 1.48E-09
Sadness | 0.3341 | 0.0109 | 0.3697 | 0.0106 | -5.3726 | 9.70E-08
Vreg |  | 0.5471 | 0.0204 | 0.4940 | 0.0170 | 6.0656 | 1.88E-09
PHEME | EIreg | Anger | 0.4547 | 0.0102 | 0.4233 | 0.0075 | 12.7093 | 1.44E-36
Fear | 0.5337 | 0.0170 | 0.5632 | 0.0198 | -8.5027 | 2.28E-17
Joy | 0.2134 | 0.0121 | 0.1817 | 0.0133 | 11.0177 | 5.58E-28
Sadness | 0.5215 | 0.0152 | 0.5177 | 0.0182 | 1.1442 | 0.2526
Vreg |  | 0.4331 | 0.0143 | 0.3842 | 0.0139 | 15.9786 | 2.18E-56
COCO | EIreg | Anger | 0.5475 | 0.0088 | 0.5641 | 0.0068 | -4.5211 | 6.43E-06
Fear | 0.5623 | 0.0097 | 0.6034 | 0.0077 | -10.5568 | 1.56E-25
Joy | 0.1800 | 0.0111 | 0.1514 | 0.0075 | 7.2230 | 6.66E-13
Sadness | 0.4701 | 0.0098 | 0.4773 | 0.0073 | -1.8808 | 0.0601
Vreg |  | 0.3961 | 0.0095 | 0.3973 | 0.0066 | -0.3325 | 0.7395
Table 3:  Statistics values of cosine similarity between embeddings of
different affective information. Top K denotes retrieval top K examples. In
addition to Vreg, the results of other affective information are all based on
top 4. "A-B" represents the calculation of cosine similarity between each data
point in A and each data point in B. Each element (i, j) in the resulting
calculation represents the cosine similarity between the i-th vector in the A
group embeddings and the j-th vector in the B group embeddings. The top 4
refers to selecting the four highest values from each row. The t-value and
p-value represent the t-test results for the "A-B" results of the two lines
above.

|  | Vreg | Voc | Ec | EIreg | EIoc
---|---|---|---|---|---|---
Datasets | Values | top 4 | top 8 | top 16 | top 32 | top 64 |  |  | anger | fear | joy | sadness | anger | fear | joy | sadness
AMT | fake-legit | 0.791 | 0.771 | 0.753 | 0.736 | 0.718 | 0.852 | 0.812 | 0.801 | 0.801 | 0.801 | 0.801 | 0.840 | 0.840 | 0.840 | 0.840
fake-fake | 0.848 | 0.810 | 0.783 | 0.761 | 0.741 | 0.894 | 0.862 | 0.855 | 0.855 | 0.855 | 0.855 | 0.885 | 0.885 | 0.885 | 0.885
t | -22.516 | -14.875 | -10.951 | -8.976 | -8.037 | -20.550 | -22.617 | -22.434 | -22.433 | -22.462 | -22.461 | -22.260 | -22.246 | -22.267 | -22.244
p | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000
legit-fake | 0.787 | 0.765 | 0.747 | 0.729 | 0.711 | 0.848 | 0.807 | 0.797 | 0.797 | 0.797 | 0.797 | 0.836 | 0.836 | 0.836 | 0.836
legit-legit | 0.841 | 0.798 | 0.768 | 0.743 | 0.721 | 0.886 | 0.856 | 0.848 | 0.848 | 0.848 | 0.848 | 0.877 | 0.877 | 0.877 | 0.877
t | -21.568 | -12.845 | -8.052 | -5.263 | -3.452 | -17.138 | -21.024 | -21.399 | -21.387 | -21.407 | -21.396 | -19.364 | -19.328 | -19.335 | -19.315
p | 0.000 | 0.000 | 0.001 | 0.008 | 0.063 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000
PHEME | nonr-rum | 0.930 | 0.927 | 0.924 | 0.921 | 0.917 | 0.982 | 0.952 | 0.940 | 0.940 | 0.940 | 0.939 | 0.972 | 0.972 | 0.972 | 0.972
nonr-nonr | 0.957 | 0.946 | 0.938 | 0.932 | 0.927 | 0.989 | 0.971 | 0.963 | 0.963 | 0.963 | 0.963 | 0.983 | 0.983 | 0.983 | 0.983
t | -75.127 | -49.017 | -35.035 | -27.844 | -24.327 | -69.237 | -78.344 | -77.082 | -77.231 | -76.869 | -78.103 | -71.392 | -71.732 | -71.005 | -72.538
p | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000
rum-nonr | 0.935 | 0.932 | 0.929 | 0.925 | 0.921 | 0.984 | 0.957 | 0.945 | 0.944 | 0.945 | 0.944 | 0.974 | 0.974 | 0.974 | 0.974
rum-rum | 0.961 | 0.950 | 0.942 | 0.935 | 0.928 | 0.990 | 0.974 | 0.966 | 0.966 | 0.967 | 0.966 | 0.984 | 0.984 | 0.984 | 0.984
t | -58.813 | -38.823 | -27.206 | -19.693 | -14.156 | -54.654 | -58.600 | -59.494 | -59.637 | -59.377 | -60.266 | -55.874 | -56.306 | -56.033 | -56.759
p | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000
COOC | rela-consp | 0.873 | 0.870 | 0.866 | 0.861 | 0.856 | 0.955 | 0.905 | 0.885 | 0.885 | 0.886 | 0.885 | 0.936 | 0.936 | 0.937 | 0.936
rela-rela | 0.907 | 0.887 | 0.875 | 0.865 | 0.857 | 0.967 | 0.931 | 0.916 | 0.916 | 0.916 | 0.916 | 0.953 | 0.953 | 0.954 | 0.954
t | -44.603 | -23.007 | -11.581 | -5.437 | -2.012 | -37.288 | -43.522 | -44.744 | -44.772 | -44.253 | -44.800 | -38.201 | -38.337 | -37.684 | -38.281
p | 0.000 | 0.093 | 0.428 | 0.457 | 0.312 | 0.004 | 0.000 | 0.000 | 0.000 | 0.001 | 0.000 | 0.001 | 0.001 | 0.002 | 0.002
consp-rela | 0.863 | 0.858 | 0.852 | 0.846 | 0.838 | 0.950 | 0.897 | 0.876 | 0.876 | 0.877 | 0.876 | 0.929 | 0.929 | 0.930 | 0.929
consp-consp | 0.911 | 0.891 | 0.878 | 0.868 | 0.859 | 0.968 | 0.933 | 0.919 | 0.919 | 0.920 | 0.920 | 0.954 | 0.954 | 0.955 | 0.954
t | -74.176 | -47.239 | -33.132 | -25.606 | -21.079 | -54.114 | -69.563 | -73.828 | -73.876 | -73.190 | -73.709 | -60.255 | -60.393 | -59.577 | -60.204
p | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000

We collect FakeNewsAMT Pérez-Rosas et al. (2018), Celebrity Pérez-Rosas et al.
(2018), PHEME Kochkina et al. (2018), and COCO Langguth et al. (2023)
datasets. The statistics of these datasets are presented in Table 1. We
combine FakeNewsAMT and Celebrity as AMTCele. For AMTCele and PHEME, each
domain will take turns being the target domain data. For COCO, we select 3 of
12 domains as target domains, and others as source domains (The details of
datasets can be found in Appendix A.1).

####  3.1.2 Affective Analysis

We firstly conduct a comprehensive affective analysis after collecting
datasets. EmoLLaMA-chat-7B, which has the best overall performance among the
EmoLLMs Liu et al. (2024c), is used for affective analysis. EmoLLaMA-chat-7B
can be used to extract the following affective dimensions (which we jointly
call affect):

(1) Emotion intensity (EIreg): For each of four different emotions (anger,
fear, joy and sadness), assign a score between 0 and 1 to represent the
intensity of emotion of the text;

(2) Emotion intensity classification (EIoc): The text can be classified into
one of four classes of the intensity of emotion (anger, fear, joy, sadness),
i.e. no/low/moderate/high emotional intensity;

(3) Sentiment (valence) strength (Vreg): Assign a real-valued score between 0
(most negative) and 1 (most positive) to represent the sentiment intensity of
the text.

(4) Sentiment (valence) classification (Voc): The text can be categorized into
one of seven ordinal classes (i.e. {very, moderately, slightly} negative,
neutral, {slightly, moderately, very} positive);

(5) Emotion detection (Ec): The text can be classified as ‘neutral or no
emotion’ or as one, or more, of eleven given emotions (anger, anticipation,
disgust, fear, joy, love, optimism, pessimism, sadness, surprise, trust).

Obtain implicit and explicit affective information: Following the guidelines
of EmoLLMs Liu et al. (2024c), we add prompts provided by EmoLLMs for each
data point in order to obtain vectors from the last hidden layer (i.e., 4096d)
for each affective dimension, as well as final labels using EmoLLaMA-chat-7B.
We subsequently determine the distribution of affective information in
different categories in each dataset.

Explicit affective analysis: Table 2 shows regression information (i.e., EIreg
and Vreg) of final labels. The t-value and p-value calculated between
legit/non-rumours/related and fake/rumours/conspiracy demonstrate that there
are statistically significant affective differences between the different
categories. Figure 3 to Figure 8 in Appendix B confirm that other
classifications using affective information are also related to
misinformation. However, we can also observe some special cases that cannot
effectively distinguish real and false information (e.g. EIreg-sadness in
PHEME and COCO, Vreg in COCO). Liu et al. (2024b) also conducted some
experiments that demonstrated that simply utilizing explicit affective
information does not enhance the model’s capability. Therefore, we introduce
how to utilize implicit affective information.

Implicit affective analysis: Table 3 shows statistics of different affective
embeddings. We perform t-tests on the top-$K$ cosine similarity within
categories and the cosine similarity between categories. For example, "fake-
legit" denotes computing the cosine similarity between each data point in the
"fake" category and each data point in the "legit" category. We then selected
the top-$K$ similarity values and performed t-test on them. The t-value and
p-value of the top-4 similarity values between "fake-legit" and "fake-fake"
are -22.516 and 0, which demonstrates that the top 4 similar data retrieved
based on cosine similarity within the "fake" category are highly likely to
belong to the same "fake" category. We can see from the results that all
affective information leads to the same conclusion in the top-4 scenarios111It
should be noted that in Vreg, as the value of $K$ increases, the second
p-value in AMTCele and the first p-value in COCO dataset also gradually
increase, which may affect the results. Therefore, we choose $K$ to be 4. The
analysis of different values of $K$ can be found in Section E.. We also
visualize the data distribution reduced to 3 dimensions using PCA in Figures 9
and 10. It can be observed that different categories are clearly separated in
the latent space. All the above demonstrates the close relationship between
affective information and misinformation.

####  3.1.3 Retrieval Database Construction

We obtained the implicit affective embeddings in the last step. Due to the
high dimensionality of the original vectors, there is a possibility of
encountering the curse of dimensionality Marimont and Shapiro (1979), which
can increase retrieval time. To address this, we employ PCA to reduce the
dimensions to 3d, 8d, 16d, 128d, and 512d, respectively (Figure 12 in Appendix
D shows the time-F1 trade-off). As a result, we construct a retrieval database
comprising vectors of six kinds of dimensions representing affective
information.

###  3.2 Retrieval Module

After constructing the retrieval database, we first obtain the target domain
corpus embedding $E_{T}=[e_{t1},e_{t2},...,e_{tN}]$ and source domain corpus
embedding $E_{S}=[e_{s1},e_{s2},...,e_{sM}]$ through the embedding retrieval
database R based on the target domain corpus
$D_{T}=[c_{t1},c_{t2},...,c_{tN}]$ and source domain corpus
$D_{S}=[c_{s1},c_{s2},...,c_{sM}]$. ($N$ and $M$ are the numbers of target
domain texts and source domain texts respectively.) Subsequently, we traverse
the target domain embeddings $E_{T}$ and calculate the similarity values with
embeddings $E_{S}$ from source domains using the cosine method. Finally, we
select the top k examples based on the similarity values that will be the few-
shot examples for LLM inference. Algorithm 1 shows the retrieval process.

Algorithm 1 Retrieval process

0: Target domain corpus $D_{T}$, source domain corpus $D_{S}$, retrieval
database $R$.

0: Target domain corpus with top K retrieval examples $D_{retri}$.

1: $E_{T}\leftarrow R(D_{T})$

2: $E_{S}\leftarrow R(D_{S})$

3: for $e_{t}$ in $E_{T}$ do

4: for $e_{s}$ in $E_{S}$ do

5: score == cosine($e_{t}$,$e_{s}$)

6: if $score>threshold(0.2)$ then

7: $Sco\leftarrow score$

8: end if

9: end for

10: $D_{retri}\leftarrow$ select top k examples in $R(D_{S})$ according to
$Sco$

11: end for

###  3.3 Inference Module

Template 1 Task: [task prompt] Target text: [input text] Here are a few
examples: [examples] According to the above information, the label of target
text: [output]

We apply template 1 to construct the instruction datasets for inference once
get top examples for each target domain data. [task prompt] denotes the
instruction for the task (The different [task prompts] for each datasets can
be found in Appendix A.2). [input text] is a data item from the target domain
data. [examples] are the retrieval examples (text-label) from source domain
data and the [output] is the output from LLM.

We also apply template 2 to add explicit affective information. [affective
information] contains five dimensions described in Section 3.1.2. The format
of [examples] is "Text: [text]. [Affective info]: [value]. The label of text:
[label]". Table 6 shows one complete example.

Template 2 Task: [task prompt] Target text: [input text] \+ [affective info]
Here are a few examples retrieved by [affective info]: [examples] According to
the above information, the label of target text: [output]

##  4 Experiments

Table 4:  Overall results on three datasets. ’zs’ denotes the zero-shot
method. ’random’ denotes randomly sample four examples without using affective
information. ’random-addexpl’ denotes adding explicit Vreg information for the
randomly sample examples. ’Vreg’ denotes retrieving four examples based on
Vreg information using Template 1. ’Vreg-addexpl’ denotes adding explicit Vreg
information using Template 2.

| AMTCele | PHEME | COCO
---|---|---|---
Model | Acc | Pre | Rec | F1 | Acc | Pre | Rec | F1 | Acc | Pre | Rec | F1
BERT | 0.5414 | 0.5453 | 0.5414 | 0.5322 | 0.7214 | 0.7203 | 0.7214 | 0.7208 | 0.7288 | 0.7510 | 0.7288 | 0.6356
RoBERTa | 0.5678 | 0.7228 | 0.5678 | 0.4730 | 0.7199 | 0.7213 | 0.7199 | 0.7204 | 0.7328 | 0.7851 | 0.7328 | 0.6388
mistral7b-zs | 0.7194 | 0.7417 | 0.7194 | 0.7127 | 0.5910 | 0.6460 | 0.5910 | 0.5956 | 0.4453 | 0.7081 | 0.4453 | 0.5391
mistral7b-random | 0.7071 | 0.7762 | 0.7071 | 0.6876 | 0.6193 | 0.6326 | 0.6193 | 0.6238 | 0.6663 | 0.7429 | 0.6663 | 0.7025
mistral7b-random-addexpl | 0.6357 | 0.7051 | 0.6357 | 0.6021 | 0.5788 | 0.6137 | 0.5788 | 0.5856 | 0.6360 | 0.7254 | 0.6360 | 0.6765
mistral7b-Vreg | 0.7510 | 0.7801 | 0.7510 | 0.7444 | 0.6761 | 0.6825 | 0.6761 | 0.6786 | 0.7395 | 0.7966 | 0.7395 | 0.7667
mistral7b-Vreg-addexpl | 0.7786 | 0.7881 | 0.7786 | 0.7767 | 0.6942 | 0.6928 | 0.6942 | 0.6935 | 0.7488 | 0.7978 | 0.7488 | 0.7725
gemma2b-zs | 0.4592 | 0.4469 | 0.4592 | 0.4259 | 0.3988 | 0.4988 | 0.3988 | 0.3742 | 0.3314 | 0.4578 | 0.3314 | 0.3845
gemma2b-random | 0.4980 | 0.4997 | 0.4980 | 0.4649 | 0.4268 | 0.5797 | 0.4268 | 0.3573 | 0.4477 | 0.6336 | 0.4477 | 0.4816
gemma2b-random-addexpl | 0.4918 | 0.4918 | 0.4918 | 0.4916 | 0.5914 | 0.5777 | 0.5914 | 0.5820 | 0.6221 | 0.6164 | 0.6221 | 0.5587
gemma2b-Vreg | 0.6367 | 0.6429 | 0.6367 | 0.6328 | 0.4643 | 0.5528 | 0.4643 | 0.4503 | 0.5291 | 0.7333 | 0.5291 | 0.5810
gemma2b-Vreg-addexpl | 0.6184 | 0.6533 | 0.6184 | 0.5953 | 0.6039 | 0.5915 | 0.6039 | 0.5953 | 0.6767 | 0.6932 | 0.6767 | 0.5990
llama3-8b-zs | 0.5143 | 0.5256 | 0.5143 | 0.4538 | 0.3757 | 0.6409 | 0.3757 | 0.2085 | 0.7198 | 0.7661 | 0.7198 | 0.6427
llama3-8b-random | 0.6235 | 0.6479 | 0.6235 | 0.6073 | 0.3788 | 0.5440 | 0.3788 | 0.2242 | 0.6686 | 0.6865 | 0.6686 | 0.6270
llama3-8b-random-addexpl | 0.5684 | 0.5685 | 0.5684 | 0.5682 | 0.3745 | 0.6616 | 0.3745 | 0.2051 | 0.6802 | 0.7718 | 0.6802 | 0.6982
llama3-8b-Vreg | 0.6408 | 0.6824 | 0.6408 | 0.6191 | 0.4682 | 0.5778 | 0.4682 | 0.4479 | 0.7151 | 0.7653 | 0.7151 | 0.6683
llama3-8b-Vreg-addexpl | 0.5786 | 0.5788 | 0.5786 | 0.5783 | 0.3855 | 0.6823 | 0.3855 | 0.2317 | 0.4488 | 0.8132 | 0.4488 | 0.5551
ChatGPT-zs | 0.7541 | 0.7649 | 0.7541 | 0.7515 | 0.5835 | 0.6483 | 0.5835 | 0.5864 | 0.7709 | 0.7938 | 0.7709 | 0.7245
ChatGPT-random | 0.7082 | 0.7459 | 0.7082 | 0.6965 | 0.6112 | 0.6494 | 0.6112 | 0.6173 | 0.7651 | 0.7808 | 0.7651 | 0.7152
ChatGPT-random-addexpl | 0.7112 | 0.7321 | 0.7112 | 0.7046 | 0.5986 | 0.6604 | 0.5986 | 0.6022 | 0.7558 | 0.7660 | 0.7558 | 0.7002
ChatGPT-Vreg | 0.6939 | 0.7578 | 0.6939 | 0.6736 | 0.6539 | 0.6622 | 0.6539 | 0.6570 | 0.8012 | 0.8091 | 0.8012 | 0.7743
ChatGPT-Vreg-addexpl | 0.6939 | 0.7326 | 0.6939 | 0.6806 | 0.6224 | 0.6795 | 0.6224 | 0.6266 | 0.8047 | 0.8114 | 0.8047 | 0.7814
GPT4o-zs | 0.8929 | 0.9003 | 0.8929 | 0.8924 | 0.6218 | 0.6633 | 0.6218 | 0.6275 | 0.8209 | 0.8482 | 0.8209 | 0.7951
GPT4o-random | 0.8796 | 0.8928 | 0.8796 | 0.8786 | 0.6511 | 0.6925 | 0.6511 | 0.6564 | 0.8547 | 0.8704 | 0.8547 | 0.8394
GPT4o-random-addexpl | 0.8612 | 0.8819 | 0.8612 | 0.8593 | 0.6134 | 0.6641 | 0.6134 | 0.6184 | 0.8628 | 0.8690 | 0.8628 | 0.8523
GPT4o-Vreg | 0.8765 | 0.8894 | 0.8765 | 0.8755 | 0.6718 | 0.7076 | 0.6718 | 0.6770 | 0.8593 | 0.8696 | 0.8593 | 0.8467
GPT4o-Vreg-addexpl | 0.8878 | 0.8984 | 0.8878 | 0.8870 | 0.6962 | 0.7310 | 0.6962 | 0.7011 | 0.8640 | 0.8745 | 0.8640 | 0.8521
Vicuna-7b-zs | 0.5306 | 0.5361 | 0.5306 | 0.5333 | 0.4931 | 0.5950 | 0.4931 | 0.4791 | 0.3337 | 0.5995 | 0.3337 | 0.3382
Vicuna-7b-random | 0.5735 | 0.5789 | 0.5735 | 0.5760 | 0.4143 | 0.5828 | 0.4143 | 0.3317 | 0.6709 | 0.6159 | 0.6709 | 0.6033
Vicuna-7b-random-addexpl | 0.5653 | 0.6030 | 0.5653 | 0.5349 | 0.5348 | 0.5764 | 0.5348 | 0.5458 | 0.6651 | 0.5754 | 0.6651 | 0.5766
Vicuna-7b-Vreg | 0.6031 | 0.6217 | 0.6031 | 0.6017 | 0.4585 | 0.6267 | 0.4585 | 0.4070 | 0.7547 | 0.7560 | 0.7547 | 0.7293
Vicuna-7b-Vreg-addexpl | 0.6082 | 0.6346 | 0.6082 | 0.6088 | 0.5866 | 0.6034 | 0.5866 | 0.5947 | 0.6826 | 0.7483 | 0.6826 | 0.6961

###  4.1 Base Models

We apply ChatGPT (gpt-3.5-turbo-0125), GPT-4o222https://openai.com/,
LLaMA3-8b-Instruct333https://llama.meta.com/llama3/, Gemma-instruct-(2b, 7b)
Team et al. (2024), Mistral-7b-Instruct Jiang et al. (2023) and Vicuna-(7b,
13b, 33b) Chiang et al. (2023) as base models to test our methods. We also
select BERT Devlin et al. (2018) and RoBERTa Liu et al. (2019) as fine-tuning
baselines. Specifically, one domain is selected as the target domain, source
domains are used as the training dataset to fine-tune.

###  4.2 Evaluation Metric

Misinformation detection is typically regarded as a classification task,
therefore we employ a variety of metrics—Accuracy, Precision, Recall, and F1
for evaluation Su et al. (2020) (All metrics use the weighted variant).

###  4.3 Results

####  4.3.1 Results of different LLMs on the data retrieved based on Vreg

Figure 2: Results of mistral7b based on different affective information on
three datasets. ’affect’ denotes retrieving four examples based on one
affective information using Template 1. ’affect-addexpl’ denotes adding
explicit affective information using Template 2.

We firstly select the instruction data based on Vreg to test the effectiveness
of the RAEmoLLM framework on different LLMs. The results in Table 4 represent
the best-performing results of retrieval using different dimensions of each
model. The result is the overall performance, which means that in AMTCele and
PHEME, every domain is considered as the target domain test set, and the
overall result is the performance of the combination of each domain test set.
For Gemma series and Vicuna series, we only show the best-performing one in
the table. In the content of this section, we will be discussing results
exclusively based on the F1 score.

Comparison with zero-shot method and random sample examples method without
using affective information (random few-shot): From Table 4, we can observe
that the RAEmoLLM framework increases the LLMs with zero-shot method largely
in most cases. The largest increase in AMTCele is gemma2b (+20.69%), in PHEME
is llama3-8b (+23.94%), and in COCO is Vicuna7b (+39.11%). The random few-shot
method also performs lower than the RAEmoLLM in most cases. Moreover, in some
cases, this method decreases the performance of the model compared to the
zero-shot method. A special case is that in the AMTCele dataset, ChatGPT’s and
GPT-4o’s performance is reduced by adding few-shot examples. One possible
reason is that the AMTCele dataset is collected from fact-checking websites,
and ChatGPT’s and GPT-4o’s training set includes these data and can
effectively utilize this information.

Comparison with fine-tuning method: We can observe that most LLMs with
RAEmoLLM framework outperform fine-tuned RoBERTa and BERT on AMTCele and COCO
datasets, but it slightly underperforms fine-tuned models in the PHEME
dataset. One possible reason is that in cross-domain misinformation detection
tasks, the fine-tuning method may perform better for simple short-text
discrimination problems (e.g. PHEME). However, the fine-tuning methods may not
be suitable for long texts (e.g. AMTCele) or complex tasks (e.g. intent
recognition in COCO).

Comparison between approaches utilizing different affective information: We
can see LLMs with explicit affective information (i.e. LLMs-Vreg-expl) exceed
LLM without explicit affective information (i.e. LLMs-Vreg) in many cases.
However, in the case of the llama3-8B and a few exceptional scenarios, it is
the opposite. And applying only explicit Vreg information (i.e. random-
addexpl) has little effect in most cases. This demonstrates that utilizing
explicit affective information places higher demands on the model, requiring
it to be able to handle not only the main text but also pay attention to
affective cues.

Table 7 and Table 8 in Appendix C present the performance of mistral7b on each
domain on AMTCele and PHEME separately. It can be observed that mistral7b with
RAEmoLLM framework overtakes mistral with zero-shot in most domains except for
prince, gurlitt, and ebola domains in PHEME, which are significant imbalanced
data.

Table 4 shows that mistral7b has the best performance among open-sourced LLMs.
We choose mistral7b to conduct the following experiments.

####  4.3.2 Results on the data retrieved based on different affective
information

Figure 2 presents the best results of retrieval with each affective dimension
embedding (Figure 11 shows all the F1 scores of mistral7b with four few-shot
examples using different affective dimension information). It is evident that
using affect-addexpl method can improve compared to solely relying on implicit
affective information retrieval (i.e. Vreg, EIreg). However, when using
affective classification information, adding explicit affective information
may confuse the model (e.g. Voc, EIoc in AMTCele and PHEME). In the COCO
dataset, all the affect-addexpl method outperforms affect except for EIreg-
anger. Regarding the affect-addexpl method, in AMTcele, we can see the results
retrieval based on Vreg are best, followed by EIreg-joy and EIreg-sadness. And
the final three rankings are retrieved based on Voc, EIoc-anger, and Ec. It
seems that affective intensity and strength are more suitable for cross-domain
fake news detection tasks. In PHEME, retrieval based on Ec exhibits the
highest performance, with the Vreg and EIreg series closely trailing behind.
While the last few are the EIoc series, which may suggest that a coarse-
grained emotional intensity classification is not suitable for rumor
detection. However, it is the opposite in the conspiracy theory dataset. In
COCO, the performance of retrieval based on the EIoc series is better than
that based on the EIreg series.

####  4.3.3 Special cases analysis

Table 5:  Special cases retrieval based on EIoc. ‘num’ denotes number. ‘non-
rum’ denotes non-rumours. ‘consp’ denotes conspiracy. The ‘0’, ‘2’, and ‘3’ in
the EIoc column represent ‘no’, ‘moderate’, and ‘high’ emotional intensity.

Datasets | EIoc | num | F1 | mean num of retrieval
---|---|---|---|---
legit/non-rum/related | fake/rumour/consp
AMTCele | fake anger=0 | 218 | 0.7718 | 1.0780 | 2.9220
legit anger=2/3 | 29 | 0.9455 | 2.2414 | 1.7586
fake joy=2/3 | 14 | 0.6000 | 1.5000 | 2.5000
legit joy=0 | 304 | 0.8824 | 2.1217 | 1.8783
PHEME | non-rum fear=2/3 | 446 | 0.6959 | 2.4776 | 1.5224
rumour fear=0 | 1039 | 0.3727 | 2.4658 | 1.5342
non-rum joy=0 | 3795 | 0.8966 | 2.9057 | 1.0943
rumour joy=2/3 | 25 | 0.2759 | 3.7600 | 0.2400
COCO | related fear=2/3 | 47 | 0.5313 | 2.0426 | 1.9574
consp fear=0 | 171 | 0.8787 | 0.9708 | 3.0292
realted joy ==0 | 246 | 0.7385 | 2.2927 | 1.7073

Misinformation and true information often convey different affective
information (as shown in Table 2 and Table 3). For example, fake news and
conspiracy theories tend to evoke more negative sentiments and emotions (e.g.
anger or fear) and less joy. However, these results are based on statistics
derived from the entire dataset. The special cases need to be analyzed. We
investigate some special cases retrieved based on EIoc. The results are listed
in Table 5.

For AMTCele, we investigate cases where fake news lacks anger or exhibits
higher levels of joy, as well as cases where legit news displays higher levels
of anger or lacks joy. We can see that the examples retrieved are mostly of
the same category as the target, and their results have not been greatly
influenced. For PHEME and COCO, we calculate statistics on cases of rumour and
conspiracy without fear or exhibiting higher levels of joy (we do not report
conspiracy with higher joy due to its low occurrence), as well as cases where
non-rumour and related display higher levels of fear or without joy. We can
see that the results for rumours in PHEME and related in COCO are relatively
poor. The most likely reason is due to the imbalance of categories in the
original data, and these special cases are in the minority. This has resulted
in the retrieval of more data from the larger category in original datasets,
causing the model to learn less useful information and ultimately affecting
the final results.

##  5 Conclusion and Future Work

In this paper, we propose RAEmoLLM, the first RAG framework to address cross-
domain misinformation detection using in-context learning based on affective
information. We introduce the three modules of RAEmoLLM. We also conduct a
comprehensive affective analysis for three public misinformation datasets. We
evaluate the performance of RAEmoLLM on the three misinformation benchmarks
based on various LLMs. The results show that RAEmoLLM can significantly
improve LLMs compared to the zero-shot method and randomly few-shot method,
which illustrates the effectiveness of RAEmoLLM. We also analyze the
performance of retrieval based on different affective information, and some
special cases, which provide a foundation for further improvements in the
future.

In the future, we will explore the application of multimodal affective
information in the task of detecting misinformation. We will also evaluate the
application of the RAEmoLLM framework in other fields (e.g. mental health and
finance). In addition to affective information, there are many other
influencing factors in misinformation, such as stance and topic. We will
combine sentiments and emotions with other features to construct a more robust
retrieval database. Furthermore, the retrieval process can be slowed down by a
large amount of data. In the future, we will also explore more efficient
retrieval methods.

##  6 Limitations

Due to restricted computational resources, we only carried out inference of
2B, 7B, 8B, 13B, and 33B open-sourced LLMs. As such, we have not considered
how the use of larger or different model architectures may potentially impact
upon performance in cross-domain misinformation detection tasks.

Though achieving outstanding performance, RAEmoLLM still bears limitations.
Firstly, for domain data with imbalanced distribution, RAEmoLLM performs worse
compared to zero-shot methods (e.g. prince, gurlitt, and ebola domains in
PHEME). The special cases analysis in Section 4.3.3 also illustrates in the
imbalanced datasets, the retrieval in RAEmoLLM will be influenced for some
special cases. Therefore, further exploration is needed to address such
issues. Secondly, in the PHEME dataset, RAEmoLLM performs worse than fine-
tuning methods without emotional information. This indicates that for simple
tasks with shorter texts, the model still struggles to effectively balance
textual features and emotional information.

##  7 Ethics Statement

The datasets we use in this paper are sourced from public social media
platforms and websites. We strictly adhere to privacy agreements and ethical
principles to protect user privacy and to ensure the proper application of
anonymity in all texts.

## Acknowledgments

This work is supported by the scholar award from the Department of Computer
Science at the University of Manchester and the Turing Scheme Fund. This work
is also supported by the Manchester-Melbourne-Toronto Research Fund, the
Centre for Digital Trust and Society at the University of Manchester, and the
New Energy and Industrial Technology Development Organization.

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##  Appendix A Datasets

###  A.1 Preprocessing of raw datasets

FakeNewsAMT is a cross-domain dataset, including six domains. The legitimate
news in FakeNewsAMT was obtained from various mainstream news websites. The
authors adopted crowdsourcing via Amazon Mechanical Turk (AMT) to generate
fake versions of legitimate news items. The Celebrity dataset was derived from
online magazines. We combine FakeNewsAMT and Celebrity as AMTCele. PHEME
contains a collection of Twitter rumors and non-rumors posted during nine
breaking events news. COCO dataset consists of 12 conspiracy theory
categories444Suppressed Cures, Behavior Control, Anti Vaccination, Fake Virus,
Intentional Pandemic, Harmful Radiation, Depopulation, New World Order,
Esoteric Misinformation, Satanism, Other Conspiracy Theory, Other
Misinformation.. Each tweet in COCO is assigned an overall intention label, as
follows: Conspiracy is assigned to tweets for which the tweet is related to at
least one of the 12 categories and is actively spreading conspiracy theories.
Otherwise, if the tweet is related to the specific category, but it does not
propagate misinformation or conspiracy theories, then the overall label of
Related is used. The overall label of Unrelated is only used for tweets that
are unrelated to all 12 conspiracy categories. We remove the Unrelated text
since the aim of the cross-domain test. For COCO dataset, we select the Fake
Virus, Harmful Radiation, and Depopulation topics as the test set, and the
other topics as the retrieval dataset.

###  A.2 Task Prompt and Instruction Dataset Example

For AMTCele, we utilize "Determine whether the target text is 0. Fake or 1.
Legit." For PHEME, we employ "Determine if the target text is 0. non-rumours
or 1. rumours." For COCO, we apply "Classify the text regarding COVID-19
conspiracy theories or misinformation into one of the following three classes:
0. Unrelated. 1. Related (but not supporting). 2. Conspiracy (related and
supporting)." Here we keep the 0\. Unrelated category to test the robustness
of the LLM by increasing the complexity of the task.

Table 6:  An example in PHEME instruction dataset. Task: Determine if the
target text is 0. non-rumours or 1. rumours.
---
Target text: UPDATE: Reports of 1 more shooter being SHOT. This is in addition
to one shot and killed earlier in Parliament Hill #OttawaShooting. Sentiment
intensity: 0.234.
Here are a few examples retrieved through sentiment intensity:
Text: UPDATE: Reports gunman says four devices are located around Sydney.
Security response underway. Police calling for calm. #9News. Sentiment
intensity: 0.429. The label of this text: 1. rumours.
Text: JUST IN: Police confirm to ABC there is a second hostage situation
underway in eastern Paris. Sentiment intensity: 0.328. The label of this text:
1. rumours.
Text: UPDATE: There are reports police have discovered the identity of the
lone gunman, with the #SydneySiege in its sixth hour. #9News Sentiment
intensity: 0.435. The label of this text: 1. rumours.
Text: JUST IN: A separate shooting and hostage situation at a supermarket in
eastern Paris has been reported … developing. Sentiment intensity: 0.236. The
label of this text: 1. rumours.
According to the above information, the label of target text:

##  Appendix B Affective analysis

We show the statistics values and distribution of labels and embeddings in
this Appendix. In Figures 3 to Figure 8, the y-axis represents the
distribution of labels within the intention class indicated on the x-axis. The
affective analysis on COCO has been done by ConspEmoLLM Liu et al. (2024b).

Figure 3: Emotion intensity classification on AMTCele Figure 4: Sentiment
classification on AMTCele Figure 5: Emotion classification on AMTCele Figure
6: Emotion intensity classification on PHEME Figure 7: Sentiment
classification on PHEME Figure 8: Emotion classification on PHEME Figure 9: 3D
visualization of affective embeddings on AMTCele. 0: Fake. 1: Legit Figure 10:
3D visualization of affective embeddings on PHEME. 0: Non-rumours. 1: Rumours

##  Appendix C The results from different domains in the AMTCele dataset and
the results from different topics in the PHEME dataset. (Table 7 and Table 8)

Table 7:  The results from different domains in the AMTCele dataset

| biz | edu | entmt | polit | sports | tech | celebrity
---|---|---|---|---|---|---|---
Model | Acc | F1 | Acc | F1 | Acc | F1 | Acc | F1 | Acc | F1 | Acc | F1 | Acc | F1
BERT | 0.5975 | 0.5930 | 0.5725 | 0.5436 | 0.5800 | 0.5610 | 0.5450 | 0.5180 | 0.5525 | 0.5293 | 0.5650 | 0.5409 | 0.5152 | 0.5039
mistral7b-zs | 0.7375 | 0.7324 | 0.8250 | 0.8222 | 0.7000 | 0.6952 | 0.5750 | 0.5248 | 0.8000 | 0.7980 | 0.6625 | 0.6260 | 0.7200 | 0.7174
mistral7b-random | 0.7250 | 0.7067 | 0.8000 | 0.7968 | 0.8250 | 0.8249 | 0.5250 | 0.3866 | 0.6750 | 0.6366 | 0.5500 | 0.4508 | 0.6260 | 0.5778
mistral7b-Vreg | 0.7750 | 0.7656 | 0.8250 | 0.8222 | 0.8250 | 0.8222 | 0.6375 | 0.5983 | 0.8125 | 0.8089 | 0.7000 | 0.6755 | 0.7400 | 0.7357
mistral7b-Vreg-addexpl | 0.8000 | 0.7968 | 0.8750 | 0.8743 | 0.8750 | 0.8743 | 0.6750 | 0.6577 | 0.8375 | 0.8373 | 0.8625 | 0.8607 | 0.7380 | 0.7366

Table 8:  The results from different domains in the PHEME dataset

| sydneysiege | ottawashooting | charliehebdo | ferguson | germanwings | prince | putinmissing | gurlitt | ebola
---|---|---|---|---|---|---|---|---|---
Model | Acc | F1 | Acc | F1 | Acc | F1 | Acc | F1 | Acc | F1 | Acc | F1 | Acc | F1 | Acc | F1 | Acc | F1
BERT | 0.7463 | 0.7418 | 0.7497 | 0.7490 | 0.7971 | 0.8113 | 0.7053 | 0.7147 | 0.7275 | 0.7260 | 0.1296 | 0.1985 | 0.5866 | 0.5297 | 0.5391 | 0.4949 | 0.5714 | 0.7220
mistral7b-zs | 0.6495 | 0.6512 | 0.6416 | 0.6418 | 0.6147 | 0.6476 | 0.4094 | 0.4213 | 0.6716 | 0.6659 | 0.7339 | 0.8315 | 0.5546 | 0.4807 | 0.4565 | 0.4518 | 0.4286 | 0.6000
mistral7b-random | 0.6822 | 0.6833 | 0.6348 | 0.6289 | 0.6950 | 0.7193 | 0.5223 | 0.5542 | 0.6162 | 0.5970 | 0.3219 | 0.4660 | 0.5420 | 0.4597 | 0.5290 | 0.4278 | 0.4286 | 0.6000
mistral7b-Vreg | 0.7215 | 0.7189 | 0.6640 | 0.6575 | 0.7321 | 0.7508 | 0.5844 | 0.6126 | 0.7271 | 0.7266 | 0.5365 | 0.6807 | 0.6008 | 0.5716 | 0.4783 | 0.4357 | 0.5000 | 0.6667
mistral7b-Vreg-addexpl | 0.7486 | 0.7449 | 0.6708 | 0.6630 | 0.7456 | 0.7635 | 0.6623 | 0.6723 | 0.7015 | 0.7009 | 0.4421 | 0.5964 | 0.6008 | 0.5983 | 0.4420 | 0.4372 | 0.4286 | 0.6000

##  Appendix D Effectiveness analysis

Figure 11: F1 score of mistral7b based on different affective dimension
information retrieval on three datasets Figure 12: Time-F1 Trade-Off. The
x-axis represents the average time (ms) consumed for retrieving the top 4
examples for each data point. The y-axis represents F1 score of mistral7b-Vreg
on three datasets. The solid line represents the results obtained only using
Vreg retrieval, while the dashed line (i.e., data-addexpl) represents the
results with the addition of explicit Vreg.

Figure 11 shows different performances of mistrail7b retrieval examples using
different affective dimension embeddings. From Figure 11, we can see in
AMTCele, the F1 score of retrieval based on 16d embeddings is similar to
original 4096d embedding retrieval, and in PHEME and COCO, the performance of
retrieval based on larger dimensions is significantly better than lower
dimensions retrieval. Figure 12 presents the Time-F1 score trade-off. We can
see the time cost of the original 4096d retrieval is significantly higher than
the time cost of other dimension retrievals. The closer to the upper left
corner (i.e., less time, higher F1 score), the better. Overall, it seems that
the LLM with 16d affective information retrieval has the highest trade-off
efficiency.

##  Appendix E Results of different numbers of retrieval examples

Table 9:  F1 score of mistral7b with retrieval of different numbers of
examples based on Vreg. ’random’ denotes randomly sampling four examples.
’Vreg’ denotes retrieval of four examples based on Vreg. ’Vreg-addexpl’
denotes adding explicit Vreg.

Datasets | methods | 4 | 8 | 16 | 32 | 64
---|---|---|---|---|---|---
AMTCele | Random | 0.6876 | 0.7110 | 0.6174 | - | -
Vreg | 0.7444 | 0.7385 | 0.7322 | - | -
Vreg-addexpl | 0.7767 | 0.7663 | 0.7680 | - | -
PHEME | Random | 0.6238 | 0.6218 | 0.6244 | 0.6389 | 0.6333
Vreg | 0.6786 | 0.6854 | 0.6821 | 0.6941 | 0.6997
Vreg-addexpl | 0.6935 | 0.6957 | 0.6965 | 0.6957 | 0.6920
COCO | Random | 0.7025 | 0.7472 | 0.7274 | 0.7524 | 0.7130
Vreg | 0.7667 | 0.7610 | 0.7739 | 0.7838 | 0.7534
Vreg-addexpl | 0.7725 | 0.7128 | 0.7190 | 0.6933 | 0.6963

Table 9 presents the F1 score of retrieval of different numbers of examples
based on Vreg (we only tested 16 examples in the AMTCele dataset due to its
long text). From the table, it can be observed that increasing the retrieval
examples does not consistently improve the model’s performance, and it may
even lead to a decline in its performance (e.g. Vreg-addexpl in COCO). One
possible reason is that when the model has multiple examples as references, it
needs to consider a large amount of information comprehensively, which depends
on the model’s capability. Another reason we can infer from Table 3. For the
three datasets, the p-values in retrieval top 4 examples are all zero.
However, as the number of retrieval examples increases, the second p-values in
AMTCele and the first p-value in COCO dataset also gradually increase. This
indicates that the retrieved content may come from another category or
unrelated examples, thereby affecting the model’s judgment ability. Therefore,
when employing retrieval augmentation techniques, it is not just about blindly
increasing the number of examples, but rather selectively choosing the most
useful examples.