Using Twitter trust network for stock market analysis

Ruan, Yefeng; Durresi, Arjan; Alfantoukh, Lina

doi:10.1016/j.knosys.2018.01.016

Cited by 109 publications

(55 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Next, they identify similar trustworthy users and finally using user-contributed QoS data of these users, they predict the QoS. Ruan et al [102] proposed a trust-aware approach for increasing the correlation between social media and financial data in the stock market. They collected stock-related data (tweets) from Twitter and they proposed a reputation-based mechanism to identify a firm's Twitter sentiment valence and its stock abnormal returns.…”

Section: ) Unsupervised Approachesmentioning

confidence: 99%

A Survey on Trust Prediction in Online Social Networks

et al. 2020

View full text Add to dashboard Cite

Level of Trust can determine which source of information is reliable and with whom we should share or from whom we should accept information. There are several applications for measuring trust in Online Social Networks (OSNs), including social spammer detection, fake news detection, retweet behaviour detection and recommender systems. Trust prediction is the process of predicting a new trust relation between two users who are not currently connected. In applications of trust, trust relations among users need to be predicted. This process faces many challenges, such as the sparsity of user-specified trust relations, the context-awareness of trust and changes in trust values over time. In this paper, we analyse the state-of-the-art in pair-wise trust prediction models in OSNs, classify them based on different factors, and propose some future directions for researchers interested in this field.

show abstract

Section: ) Unsupervised Approachesmentioning

confidence: 99%

A Survey on Trust Prediction in Online Social Networks

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Influential people posting on social media can influence others to either comply with the intent behind a given social media post or oppose its purpose, which can be detrimental to sales [40]. Ruan et al [66] contended that the public mood or sentiment expressed on social media is directly related to the ups and downs in the financial markets. However, as in the case of most innovations, this inherent power of social media sentiment can also be abused [40].…”

Section: ) Sentiment Analysismentioning

confidence: 99%

Measuring Reputation and Influence in Online Social Networks: A Systematic Literature Review

et al. 2020

View full text Add to dashboard Cite

Online Social Networks (OSNs) are an integral part of the modern lifestyle, and the use of OSN is highly prevalent today. OSNs have enabled the generation of unprecedented quantities of data on users, their interests, relationships, actions, opinions, locations visited, items purchased, and their experiences in various aspects of life. Data and information related to reputation and influence exist on OSNs and are hidden. The objective of this study is to provide a better and deeper understanding and a detailed review of the current state of research pertaining to the measurement of reputation and influence in OSNs, specifically Twitter. We conducted a Systematic Literature Review to collect, analyze, and synthesize data on the accuracy and value of previous literature that has focused on this field, has addressed a variety of topics, and has been published in digital databases between 2010 and September 2019. We followed a predefined review procedure that integrated both automatic and manual search strategies to cover all relevant research papers published in this period.

show abstract

“…Recent studies have found that the relationship between social media sentiments and stock returns is time-varying (Ho et al, 2017) and some have successfully incorporated investors' emotions from social media into predicting stocks' prices (Zhou et al, 2017b;Sun et al, 2017;Ruan et al, 2018;Li et al, 2014). In this paper, we use the emotion measures from Zhou et al (2017b).…”

Section: Compositional Predictor: Market Emotionmentioning

confidence: 99%

“…For example, the direct sources originate in the financial system itself, such as the price information at various frequencies (Harris, 1986;Jain and Joh, 1988;Pan et al, 2017), the companies financial reports (Jones and Litzenberger, 1970;Zhou et al, 2015Zhou et al, , 2017a, and financial news (Geva and Zahavi, 2014;Li et al, 2014;Hagenau et al, 2013). The indirect sources are those outside the financial system, like the rise and fall of macro economic (Chen et al, 1986), the reactions and reflections from investors' emotion revealed by social media (Zhou et al, 2017b;Sun et al, 2017;Ruan et al, 2018;Zhang et al, 2017;Li et al, 2014), search engine (Preis et al, 2013) or analyst's recommendations (Duan et al, 2013), etc. The richness of data sources has provided the chances to understand the stock market more comprehensively and make the price prediction more accurate than before.…”

Section: Introductionmentioning

confidence: 99%

Aggregating multiple types of complex data in stock market prediction: A model-independent framework

Wang

Zhao

2019

Knowledge-Based Systems

View full text Add to dashboard Cite

The increasing richness in volume, and especially types of data in the financial domain provides unprecedented opportunities to understand the stock market more comprehensively and makes the price prediction more accurate than before. However, they also bring challenges to classic statistic approaches since those models might be constrained to a certain type of data. Aiming at aggregating differently sourced information and offering type-free capability to existing models, a framework for predicting stock market of scenarios with mixed data, including scalar data, compositional data (pie-like) and functional data (curvelike), is established. The presented framework is model-independent, as it serves like an interface to multiple types of data and can be combined with various prediction models. And it is proved to be effective through numerical simulations. Regarding to price prediction, we incorporate the trading volume (scalar data), intraday return series (functional data), and investors' emotions from social media (compositional data) through the framework to competently forecast whether the market goes up or down at opening in the next day. The strong explanatory power of the framework is further demonstrated. Specifically, it is found that the intraday returns impact the following opening prices differently between bearish market and bullish market. And it is not at the beginning of the bearish market but the subsequent period in which the investors' "fear" comes to be indicative. The framework would help extend existing prediction models easily to scenarios with multiple types of data and shed light on a more systemic understanding of the stock market.

show abstract

Using Twitter trust network for stock market analysis

Cited by 109 publications

References 56 publications

A Survey on Trust Prediction in Online Social Networks

A Survey on Trust Prediction in Online Social Networks

Measuring Reputation and Influence in Online Social Networks: A Systematic Literature Review

Aggregating multiple types of complex data in stock market prediction: A model-independent framework

Contact Info

Product

Resources

About