There is a consensus about the good sensing characteristics of Twitter to
mine and uncover knowledge in financial markets, being considered a relevant
feeder for taking decisions about buying or holding stock shares and even for
detecting stock manipulation. Although Twitter hashtags allow to aggregate
topic-related content, a specific mechanism for financial information also
exists: Cashtag. However, the irruption of cryptocurrencies has resulted in a
significant degradation on the cashtag-based aggregation of posts.
Unfortunately, Twitter' users may use homonym tickers to refer to
cryptocurrencies and to companies in stock markets, which means that filtering
by cashtag may result on both posts referring to stock companies and
cryptocurrencies. This research proposes automated classifiers to distinguish
conflicting cashtags and, so, their container tweets by analyzing the
distinctive features of tweets referring to stock companies and
cryptocurrencies. As experiment, this paper analyses the interference between
cryptocurrencies and company tickers in the London Stock Exchange (LSE),
specifically, companies in the main and alternative market indices FTSE-100 and
AIM-100. Heuristic-based as well as supervised classifiers are proposed and
their advantages and drawbacks, including their ability to self-adapt to
Twitter usage changes, are discussed. The experiment confirms a significant
distortion in collected data when colliding or homonym cashtags exist, i.e.,
the same \$ acronym to refer to company tickers and cryptocurrencies. According
to our results, the distinctive features of posts including cryptocurrencies or
company tickers support accurate classification of colliding tweets (homonym
cashtags) and Independent Models, as the most detached classifiers from
training data, have the potential to be trans-applicability (in different stock
markets) while retaining performance.

本研究提出了自动分类器来区分冲突的 $ 标签并通过分析涉及股票公司和加密货币的推文的独特特征来区分它们的容器推文。实验结果确认了当存在冲突或同音异义的 $ 标签时，所收集数据的显著失真。根据我们的结果，包括加密货币和股票公司标签的推文的独特特征支持冲突推文（同音异义的 $ 标签）的准确分类，独立模型作为最脱离训练数据的分类器具有在不同股票市场中适用的潜力，同时保持性能。