The Emotional Edge in Argumentation
How emotions shape the strength of arguments and persuasion.
― 5 min read
Table of Contents
In the world of debates and Arguments, Emotions play a significant role. You might not realize it, but how we feel while arguing can change how effective our argument is. This article dives into the relationship between emotions and the Persuasive power of arguments.
The Importance of Emotions in Arguments
When people present their points, it’s not just about cold hard facts. Emotions can sway opinions more than a well-researched study. If you’ve ever heard someone emotionally describe a situation, you know that it sticks with you. That’s the power of emotional arguments.
Research shows that emotions can affect how we think and feel about a topic. Positive emotions like joy and pride can make us more open to changing our opinions. In contrast, Negative emotions like anger can lead people to become defensive. When people feel happy or proud, they’re more likely to engage in discussions positively.
The Gap in Research
While there has been a lot of study on emotions, much of that work has looked at two options: positive or negative. However, emotions are a lot more complex than just two choices. We experience a variety of feelings that can also be grouped into specific categories, such as fear, joy, or disgust. Unfortunately, there hasn’t been much research investigating these specific emotions in the context of arguments.
What We Did
To bridge this gap, researchers gathered a group of people and asked them to look at different arguments. They then labeled the emotions they thought were present in those arguments. The focus was on the German language, using various arguments on different topics.
Once the human annotators labeled the emotions, they tested different ways of getting computers to understand and label these emotions in the same arguments. They used large language models, which are computer programs built to understand human language, to see if the model could match up with how humans labeled emotions.
The Experiment
The research used three different language models and tried out three different ways to prompt the models for their predictions. Think of it like giving a student three different types of last-minute study guides before an exam — some might excel with one guide over another.
The models were tested under three conditions:
- Binary emotionality: Checking if an argument has any emotion at all.
- Closed-domain: Identifying a specific emotion from a set list.
- Open-domain: Figuring out what emotion is present without a specific list to choose from.
Findings on Emotion Predictions
What did they find? The results showed that the models were pretty good at identifying emotions in arguments, but they struggled when it came to precision. In simple terms, they were like a friend who always gives you advice but often gets the details wrong. The models would recognize emotions but often labeled them inaccurately. For instance, they were particularly biased toward identifying negative emotions like fear and anger more than others.
Emotions and Persuasiveness
The study also explored how the type of emotion expressed in an argument affected how convincing that argument was. As you might expect, arguments that contained positive emotions were more convincing. Joy and pride were the champions here, while anger and fear were more likely to turn people off. If you want to convince someone, sprinkle in some joy and pride, and leave the fear and anger for horror movies.
Breaking Down the Findings
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Emotion Categories Matter: The research highlights the importance of breaking down emotions into categories. Whereas researchers often measure just positive or negative emotions, it’s essential to investigate specific feelings like anger or joy.
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Human vs. Machine: The gap in emotional labeling between human annotators and the language models shows that even advanced machines struggle to understand human feelings fully.
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The Bias Toward Negativity: The tendency of the models to focus on negative emotions leads to a skewed understanding of how arguments may be perceived. This bias can impact how arguments are framed in discussions.
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Emotional Arguments Are Stronger: Emotions, especially positive ones, enhance the convincingness of arguments. Being aware of which emotions to evoke can boost your ability to persuade others.
What’s Next?
The study leaves some questions open for future exploration. How can we improve machine understanding of emotions? One suggestion is to fine-tune the models to better capture the nuances of emotions. Just like a chef needs to adjust flavors, language models might need a bit of tweaking to serve up the right emotional responses.
The Challenge of Subjectivity
One challenge highlighted in the research is the subjective nature of emotions. Different people can feel different things in response to the same argument. It's like watching a comedy; one person might laugh while another might just shake their head. This variability makes it hard to pin down exactly what emotion someone is feeling based on their response to an argument.
Conclusion
In the grand game of arguments, emotions are the cards we play. Understanding the fine line between different emotions can help us not only in our own arguments but also in shaping how we communicate with others. As researchers strive to bridge the gap between human emotions and machine understanding, we can look forward to a future where arguments are both more emotional and more effective.
A Little Humor
So, next time you’re in a debate, remember: it’s not just about who has the best facts—it’s about who can make the other side feel a bit more joy and a lot less fear. After all, as the saying goes, “An argument without emotion is like a sandwich without bread—dry and hard to swallow!”
Original Source
Title: Fearful Falcons and Angry Llamas: Emotion Category Annotations of Arguments by Humans and LLMs
Abstract: Arguments evoke emotions, influencing the effect of the argument itself. Not only the emotional intensity but also the category influence the argument's effects, for instance, the willingness to adapt stances. While binary emotionality has been studied in arguments, there is no work on discrete emotion categories (e.g., "Anger") in such data. To fill this gap, we crowdsource subjective annotations of emotion categories in a German argument corpus and evaluate automatic LLM-based labeling methods. Specifically, we compare three prompting strategies (zero-shot, one-shot, chain-of-thought) on three large instruction-tuned language models (Falcon-7b-instruct, Llama-3.1-8B-instruct, GPT-4o-mini). We further vary the definition of the output space to be binary (is there emotionality in the argument?), closed-domain (which emotion from a given label set is in the argument?), or open-domain (which emotion is in the argument?). We find that emotion categories enhance the prediction of emotionality in arguments, emphasizing the need for discrete emotion annotations in arguments. Across all prompt settings and models, automatic predictions show a high recall but low precision for predicting anger and fear, indicating a strong bias toward negative emotions.
Authors: Lynn Greschner, Roman Klinger
Last Update: 2024-12-20 00:00:00
Language: English
Source URL: https://arxiv.org/abs/2412.15993
Source PDF: https://arxiv.org/pdf/2412.15993
Licence: https://creativecommons.org/licenses/by-nc-sa/4.0/
Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.
Thank you to arxiv for use of its open access interoperability.