Explaining the Effect of Social Media Sentiment Analysis on Investment Decisions and Stock Market Volatility through an Artificial Intelligence Approach
Keywords:
Sentiment analysis, social media, investment decisions, stock market volatility, artificial intelligence, investor behaviorAbstract
This study aimed to explain how social media sentiment analysis influences investment decisions, investor behavior, and stock market volatility, with particular emphasis on the capabilities and challenges of artificial intelligence in interpreting market-related emotional data. This study employed a qualitative exploratory design. The participants consisted of 24 active investors, capital market analysts, portfolio managers, financial technology specialists, and artificial intelligence experts based in Tehran. Participants were selected through purposive and snowball sampling. Data were collected exclusively through semi-structured interviews, and sampling continued until theoretical saturation was achieved. The interviews were transcribed verbatim and analyzed using NVivo software based on the six-phase thematic analysis procedure. To enhance trustworthiness, member checking, peer review, analytic memoing, audit trail documentation, and constant comparison of codes were applied. Data analysis led to the identification of five main categories: “emotionalization of investor attention,” “network effects of rumor, excitement, and herd behavior,” “amplification or moderation of market volatility through intelligent algorithms,” “trust, bias, and manipulation challenges in sentiment data,” and “the need for analytical governance and digital financial literacy.” Participants emphasized that social media sentiment, particularly during periods of uncertainty, can reshape risk perception, accelerate buy and sell decisions, increase trading volume, and intensify short-term market volatility. At the same time, AI-based sentiment analysis, when properly validated, may serve as a complementary decision-support and early-warning tool. The findings indicate that social media sentiment analysis should not be regarded as a substitute for fundamental or technical analysis, but rather as an additional analytical layer for understanding market behavior. Its effective application requires the integration of artificial intelligence models with human judgment, data-bias control, algorithmic transparency, and the development of investors’ digital financial literacy
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