Power of Influencers: Crowd Management with LLMs


Maximilian Schneider Avatar



Crowd Management with LLMs

In the digital age, influencers wield significant power, capable of swaying opinions, driving trends, and even shaping markets with a single post. Their reach and impact are undeniable, making them invaluable assets in various scenarios, from marketing campaigns to social movements. However, managing the crowds they attract can be a complex task. Enter Large Language Models (LLMs), the latest advancement in artificial intelligence, poised to revolutionize how we manage and leverage influencer-driven crowds.

The Influencer Effect: A Double-Edged Sword

Influencers, with their vast follower bases, can mobilize crowds in unprecedented ways. Whether it’s launching a new product, promoting a cause, or simply sharing a lifestyle tip, their influence can spark immediate and widespread action. However, this power comes with challenges:

  1. Volume and Velocity: The sheer volume of interactions can be overwhelming. When an influencer with millions of followers endorses a product, the ensuing flood of comments, questions, and reactions can be hard to manage in real-time.
  2. Diversity of Opinions: Influencer audiences are diverse, encompassing various demographics, cultures, and viewpoints. Managing this heterogeneity effectively requires nuanced and sensitive engagement.
  3. Potential for Misinformation: Influencers can inadvertently spread misinformation. Correcting and managing these situations swiftly is crucial to maintaining credibility and trust.

Main Findings and Implications for LLMs and Crowd Wisdom

Summary: The paper by Kakhbod et al. (2023) analyzes the quality of investment advice provided by financial influencers, or “finfluencers,” on social media platforms. Using tweet-level data from StockTwits, the researchers classified finfluencers into skilled, unskilled, and antiskilled categories. They found that 28% of finfluencers are skilled, generating positive abnormal returns, while 56% are antiskilled, generating negative abnormal returns. The study reveals that antiskilled finfluencers have more followers and greater influence on retail trading than skilled ones, often leading to overly optimistic beliefs and inefficient market prices. The findings suggest that while skilled finfluencers provide valuable advice, their influence is overshadowed by antiskilled finfluencers, highlighting the challenge of identifying reliable sources of financial information on social media.

  1. Finfluencer Classification:
    • Skilled (28%): Generate 2.6% monthly abnormal returns.
    • Unskilled (16%): No significant impact.
    • Antiskilled (56%): Generate -2.3% monthly abnormal returns.
  2. Influence and Popularity:
    • Antiskilled finfluencers have more followers and greater influence on retail trading than skilled ones.
    • Positive tweets from antiskilled finfluencers lead to overly optimistic beliefs, while negative tweets have less impact.
  3. Behavioral Traits:
    • Antiskilled finfluencers exhibit return-chasing and momentum-riding behaviors.
    • Skilled finfluencers are contrarian, tweeting positively after negative returns and negatively after positive returns.
  4. Impact on Retail Trading:
    • Antiskilled finfluencers significantly affect retail order imbalances, leading to inefficient market prices.
    • Following antiskilled finfluencers’ advice results in excessive trading and belief biases among followers.
  5. Market Implications:
    • An investment strategy contrarian to antiskilled finfluencers’ recommendations yields 1.2% monthly out-of-sample performance.
    • The presence of antiskilled finfluencers highlights the challenge for retail investors in identifying reliable financial advice on social media.

Paper – Finfluencers Summary

  1. Quality of Finfluencers’ Advice: The study categorizes financial influencers (finfluencers) into skilled, unskilled, and antiskilled groups. Approximately 28% of finfluencers are skilled, providing valuable investment advice leading to an average monthly abnormal return of 2.6%. In contrast, 16% are unskilled, and 56% are antiskilled, whose advice leads to negative returns averaging -2.3% monthly.
  2. Influence and Popularity: Surprisingly, antiskilled finfluencers tend to have more followers and greater influence on retail trading than skilled ones. This suggests that social media users may prioritize other traits over actual financial acumen when choosing whom to follow.
  3. Belief Bias and Trading Behavior: The advice of antiskilled finfluencers often leads to overly optimistic beliefs among their followers, contributing to excessive trading and inefficient market prices. Retail investors influenced by these finfluencers engage in trades that result in negative returns.
  4. Contrarian Strategy: A contrarian strategy, which involves trading against the advice of antiskilled finfluencers, yields an average monthly out-of-sample performance of 1.2%.
  5. Persistence of Skills: The skills of finfluencers, particularly those classified as skilled or antiskilled, are persistent over time. This persistence underscores the reliability of the skill classification over different periods.
  6. Social Media Behavior and Identification of Skill: Skilled finfluencers tend to tweet less frequently and post more negative tweets compared to their unskilled or antiskilled counterparts. Retail investors can, in theory, identify skilled finfluencers based on their tweeting patterns and performance metrics.

Enter Large Language Models (LLMs)

LLMs, like OpenAI’s GPT-4, are designed to understand and generate human-like text. Their ability to process and respond to vast amounts of data makes them ideal for managing influencer-driven scenarios. Here’s how LLMs can be applied:

1. Real-Time Engagement and Moderation

LLMs can be deployed to monitor and engage with audiences in real-time. By analyzing comments and questions, they can provide immediate, relevant responses, ensuring that no query goes unanswered. This is particularly useful during live events or product launches, where timely interaction is crucial.

2. Sentiment Analysis and Feedback

Understanding the sentiment of the crowd is vital. LLMs can analyze text to gauge public opinion, identifying positive, negative, or neutral sentiments. This information can help brands and influencers tailor their strategies, addressing concerns promptly and reinforcing positive feedback.

3. Content Creation and Personalization

Creating content that resonates with diverse audiences is challenging. LLMs can assist by generating personalized messages and content that cater to different segments of an influencer’s audience. This ensures that communication remains relevant and engaging, enhancing the overall impact.

4. Crisis Management

In cases where misinformation spreads, LLMs can swiftly identify and correct false information, minimizing damage. Their ability to generate clear, accurate responses helps maintain credibility and trust.

5. Data-Driven Insights

LLMs can analyze interactions to provide insights into audience behavior and preferences. This data-driven approach allows influencers and brands to refine their strategies, improving engagement and effectiveness over time.

Practical Applications: A Glimpse into the Future

The potential applications of LLMs in influencer-driven scenarios are vast and varied. Here are a few examples:

Marketing Campaigns

Brands can leverage LLMs to manage large-scale marketing campaigns, ensuring that audience interactions are handled efficiently. From answering product-related queries to personalizing marketing messages, LLMs can enhance the overall customer experience.

Social Movements

Influencers often play pivotal roles in social movements. LLMs can help manage the influx of interactions, providing accurate information and supporting positive dialogue. This ensures that the movement’s message remains clear and impactful.

Event Management

For events promoted by influencers, LLMs can handle RSVPs, provide event details, and engage with attendees before, during, and after the event. This seamless interaction enhances attendee experience and ensures smooth event execution.

Ethical Considerations and Challenges

While the potential of LLMs is immense, it’s crucial to address ethical considerations. Ensuring the accuracy and fairness of responses, maintaining user privacy, and preventing the spread of misinformation are paramount. Moreover, the human touch in interactions should not be entirely replaced; LLMs should complement, not replace, human engagement.

Conclusion: A Synergistic Future

The synergy between influencers and LLMs represents a new frontier in crowd management. By harnessing the power of AI, we can navigate the complexities of influencer-driven scenarios more effectively, enhancing engagement, fostering positive interactions, and driving meaningful outcomes. As we move forward, the collaboration between human influence and artificial intelligence promises to unlock new possibilities, transforming how we connect, communicate, and create impact in the digital age.


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