Why we don’t use personal account tracing on social media
Using event extraction from a personal account on Twitter can be a risky method for news sentiment assessment. This week, we got a very good example of why we don’t cover personal twitter account on our platform.
On Aug 07, Tesla CEO Alon Musk tweets he is ‘considering’ taking the company private, investor react immediately and increase and entering the stock which sends the share price up. For companies that monitoring Along Musk account, this event is a very bullish one, but the first red flag was the distribution of the news as SEC and regulation forbade distribution key events via other platforms rather than the regulated one. therefore, the outcome was inevitable, after 2 days the gaining from this event was gone as for the investors that jump in on Aug 07 it is a sure loss.
A personal account on twitter or other social media holds a tremendus risk of misleading event detection. First, it is not allowed to share important news and events about public companies outside the regulated filling method. Second, a personal account can be hacked and could be used in the fake news which will cause a damage for investors that relly on news.finally, personal accounts could hold a non conformed event which can effect the sentiment model when evaluating new events from the news.
Using twitter monitoring as a news events source can be beneficial, at FIRST TO INVEST we use corporate and financial publication account the see and analyze the news and event flow and usually you can get a fast detection of a new event after the proper filling is made and before the financial publication report on them:
“FT Exclusive: Saudi Arabia’s sovereign wealth fund has built a $2bn stake in Tesla https://on.ft.com/2MaSSB1”
“Tesla starts hiring for new $2 billion Shanghai plant https://yhoo.it/2ngsoA1 pic.twitter.com/MFhzyQ6VFR.”
To conclude, the next time your firm evaluate a data provider that cover social media, ask about the methodology of evaluating events for social media in order to reduce the risk and losses
(news sentiment score is scaled between 0-1 where bellow 0.5 consider as bearish sentiment based on events during the filtered period, above 0.5 consider bullish)
Generating alpha using news sentiment on extracted events, presented on a visual analytics platform
Using big data and NLP technologies to generate alpha by collecting, structure and reveal hidden insights from unstructured text within tens of thousands of news articles, press releases, and financial social media. Our data is presenting on visually compelling, interactive dashboards on your PC and mobile.
Using a hybrid model to confirm the accuracy of data classification, context, and sources to ensure maximum value for our clients. Generating news sentiment score based on relevancy, position, and strength of the signal on any level. Furthermore, connect additional datasets to leverage your analysis and insights in your secure environment.
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