This study investigates the relationship between tweet sentiment across
diverse categories: news, company opinions, CEO opinions, competitor opinions,
and stock market behavior in the biotechnology sector, with a focus on
understanding the impact of social media discourse on investor sentiment and
decision-making processes. We analyzed historical stock market data for ten of
the largest and most influential pharmaceutical companies alongside Twitter
data related to COVID-19, vaccines, the companies, and their respective CEOs.
Using VADER sentiment analysis, we examined the sentiment scores of tweets and
assessed their relationships with stock market performance. We employed ARIMA
(AutoRegressive Integrated Moving Average) and VAR (Vector AutoRegression)
models to forecast stock market performance, incorporating sentiment covariates
to improve predictions. Our findings revealed a complex interplay between tweet
sentiment, news, biotech companies, their CEOs, and stock market performance,
emphasizing the importance of considering diverse factors when modeling and
predicting stock prices. This study provides valuable insights into the
influence of social media on the financial sector and lays a foundation for
future research aimed at refining stock price prediction models.

本研究调查了生物技术领域中推文情感与各类别的关系：新闻、公司观点、CEO 观点、竞争对手观点以及股市行为，重点在于了解社交媒体话语对投资者情绪和决策过程的影响。我们分析了十家最大和最有影响力的制药公司的历史股市数据，以及与 COVID-19、疫苗、公司和相关 CEO 有关的 Twitter 数据。利用 VADER 情感分析，我们检查了推特情感得分，并评估它们与股市表现之间的关系。我们采用 ARIMA（自回归移动平均）和 VAR（向量自回归）模型来预测股市表现，并引入情感协变量以提高预测准确性。研究结果显示推文情感、新闻、生物技术公司、其 CEO 和股市表现之间存在复杂的相互作用，强调了在建模和预测股价时考虑多种因素的重要性。本研究为社交媒体对金融行业的影响提供了有价值的见解，并为进一步完善股价预测模型的研究奠定了基础。

推特对市场趋势的影响：分析社交媒体情绪对生物科技股的影响

Tweet Influence on Market Trends: Analyzing the Impact of Social Media  Sentiment on Biotech Stocks

Social media discourse from US politicians frequently consists of 'seemingly
similar language used by opposing sides of the political spectrum'. But often,
it translates to starkly contrasting real-world actions. For instance, "We need
to keep our students safe from mass shootings" may signal either "arming
teachers to stop the shooter" or "banning guns to reduce mass shootings"
depending on who says it and their political stance on the issue. In this
paper, we define and characterize the context that is required to fully
understand such ambiguous statements in a computational setting and ground them
in real-world entities, actions, and attitudes. To that end, we propose two
challenging datasets that require an understanding of the real-world context of
the text to be solved effectively. We benchmark these datasets against
baselines built upon large pre-trained models such as BERT, RoBERTa, GPT-3,
etc. Additionally, we develop and benchmark more structured baselines building
upon existing 'Discourse Contextualization Framework' and 'Political Actor
Representation' models. We perform analysis of the datasets and baseline
predictions to obtain further insights into the pragmatic language
understanding challenges posed by the proposed social grounding tasks.

社交媒体上的言论常常由政治光谱两极中的不同方面使用 “看似相似的语言” 组成，但往往对应着截然不同的现实行动。本文旨在在计算环境中定义和描述理解此类模棱两可陈述所需的背景，并将其与真实世界的实体、行动和态度联系起来。为了达到这个目标，我们提出了两个具有挑战性的数据集，需要对文本的现实背景有全面的理解才能有效解决。我们通过构建基于 BERT、RoBERTa、GPT-3 等大型预训练模型的基线，并基于现有的 “话语语境框架” 和 “政治参与者表示” 模型开发和基准测试了更有结构化的基线。我们对数据集和基线预测进行分析，以深入了解所提出的社交背景任务所带来的实用语言理解挑战。