Utilizing comment metrics for author identification.

Designers should target investment platforms, design author rankings using comment counts, as it effectively identifies top analysts.

About this paper

The author studies the identification of experts in SeekingAlpha and StockTwits by analyzing datasets from these platforms.

They find that while general content has minimal correlation to stock performance, specific expert content can be identified through user interactions and significantly outperform broader markets.

Here are some methods used in this study:

Sentiment Analysis Correlation Analysis

Which part of the paper did the design guideline come from?

“We evaluate trading strategies generated using a combination of author ranking heuristics and long vs. long/short trading strategies. For our author ranking heuristics, we use average return per article (PerA), average return per stock (PerS), number of total comments (AllCom), and average comments per article (AvgCom). As described above, we choose 500 stocks mentioned by the top ranked authors, and split the funds of a hypothetical portfolio evenly among them. Each week, we trade them based (...)” (‘Empirical Evaluation’ section)

Wang, G., Wang, T., Wang, B., Sambasivan, D., Zhang, Z., Zheng, H., & Zhao, B. Y. (2015). Crowds on Wall Street. Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing.

Inspiration and scope

In this paper, authors examined SeekingAlpha and StockTwits users to find high-performing authors via active engagement.

You are designing for fintech influencers who post high-quality finance content on social media. Both your design context and the paper's context need a mechanism to evaluate content quality. Academic methods use performance metrics for high-quality content; your design can highlight or promote reliable financial content.

Also, both benefit from efficient data utilization. Monitoring user engagement and performance aids both academic and social media strategies, helping influencers understand their impact and refine methods.

Leverage these similarities to design a feature that evaluates financial content based on user engagement metrics. Influencers can gain insights into impactful content, promoting high-quality, reliable financial info to a broader audience.

Your input

  • What: A social media platform that allow financial influencers create and post high quality finance related content.
  • Who: Fintech influencers
  • Design stage: Research, Ideation

Understanding users

The following user needs and pain points may apply to your design target as well:

Quality Content Evaluation

Develop an advanced evaluation system to ensure financial influencers produce and share reliable, high-quality content. Highlighting trustworthy content can establish a credible platform, attracting a knowledgeable audience and fostering trust.

Efficient Data Utilization

Incorporate robust analytics to monitor user engagement and content performance, enabling influencers to refine their strategies. This data-driven approach can optimize content effectiveness and increase user impact.

Design ideas

Consider the following components for your design:

1

Implement a real-time dashboard for influencers to monitor their engagement metrics.

2

Incorporate a 'Top Insights' section that features posts based on the quality assessment metrics.

3

Offer periodic data-driven reports to influencers, summarizing the performance and engagement trends of their posts.

Methods for you

Consider the following method(s) used in this paper for your design work:

Sentiment Analysis

This method helps in extracting sentiments (positive or negative opinions) from user-generated content, enabling insights into user sentiments towards certain topics. For designers, it aids in understanding user reception towards finance-related content, but it's essential to ensure accurate sentiment extraction to avoid misinterpretation.

User Survey

Conducting user surveys helps gather qualitative data directly from target users, offering insights into user needs, preferences, and trust levels. When utilizing this method, designers should ensure a diverse and representative user sample for reliable and generalizable findings.

[Figure 7] From this figure, you can explore how top performers influence market sentiments, which might inspire ways to leverage influential fintech influencers on social media.