Does analytics based on sentiment analysis of changes in Internet user opinion using Big Data Analytics help detect fakenews spread as part of the deliberate spread of disinformation on social media?
The spread of disinformation on social media used by setting up fake profiles and spreading fakenews on these media is becoming increasingly dangerous in terms of the security of not only specific companies and institutions but also the state. The various social media, including those dominating this segment of new online media, however, differ considerably in this respect. The problem is more acute in the case of those social media which are among the most popular and on which mainly young people function, whose world view can be more easily influenced by factual information and other disinformation techniques used on the Internet. Currently, among children and young people, the most popular social media include Tik Tok, Instagram and YouTube. Consequently, in recent months, the development of some social media sites such as Tik Tok is already being restricted by the governments of some countries by banning the use, installation of this application of this portal on smartphones, laptops and other devices used for official purposes by employees of public institutions. These actions are argued by the governments of these countries in order to maintain a certain level of cyber security and reduce the risk of surveillance, theft of data and sensitive, strategic and particularly security-sensitive information of individual institutions, companies and the state. In addition, there have already been more than a few cases of data leaks on other social media portals, telecoms, public institutions, local authorities and others based on hacking into the databases of specific institutions and companies. In Poland, however, the opposite is true. Not only does the organised political group PIS not restrict the use of Tik Tok by employees of public institutions, but it also motivates the use of this portal by politicians of the ruling PIS option to publish videos as part of the ongoing electoral campaign, which would increase the chances of winning parliamentary elections for the third time in autumn this year 2023. According to analysts researching the problem of growing disinformation on the Internet, in highly developed countries it is enough to create 100 000 avatars, i.e. non-existent fictitious persons, created as it were and seemingly functioning thanks to the Internet by creating profiles of these fictitious persons on social media portals referred to as fake profiles created and functioning on these portals, to seriously influence the world view, the general social awareness of Internet users, i.e. usually the majority of citizens in the country. On the other hand, in third world countries, in countries with undemocratic systems of power, all that is needed for this purpose is about 1,000 avatars of these fictitious people with stories modelled, for example, on famous people such as, in Poland, a well-known singer claiming that there is no pandemic and that vaccines are an instrument for increasing control of citizens by the state. The analysis of changes in the world view of Internet users, changes in trends concerning social opinion on specific issues, evaluations of specific product and service offers, brand recognition of companies and institutions can be conducted on the basis of sentiment analysis of changes in the opinion of Internet users using Big Data Analytics. Consequently, this type of analytics can be applied and of great help in detecting factual news disseminated as part of the deliberate spread of disinformation on social media.
In view of the above, I address the following question to the esteemed community of scientists and researchers:
Does analytics based on sentiment analysis of changes in the opinions of Internet users using Big Data Analytics help in detecting fakenews spread as part of the deliberate spread of disinformation on social media?
What is your opinion on this topic?
What is your opinion on this subject?
Please respond,
I invite you all to discuss,
Thank you very much,
Warm regards,
Dariusz Prokopowicz
Yes, sentiment analysis based on Big Data Analytics can help in detecting fake news spread as part of the deliberate spread of disinformation on social media. Sentiment analysis involves the use of natural language processing and machine learning techniques to analyze large amounts of textual data, such as social media posts, to identify the sentiment expressed in the text. By analyzing changes in the sentiment of Internet users towards a particular topic or event, it is possible to identify patterns of misinformation and disinformation.
For example, if there is a sudden surge in negative sentiment towards a particular politician or political party, it could be an indication of a disinformation campaign aimed at spreading negative propaganda. Similarly, if there is a sudden increase in positive sentiment towards a particular product or service, it could be an indication of a paid promotion or marketing campaign.
However, it is important to note that sentiment analysis alone may not be enough to detect fake news and disinformation. It is also important to consider other factors such as the source of the information, the credibility of the information, and the context in which the information is being shared. Therefore, a comprehensive approach involving multiple techniques and tools may be necessary to effectively detect and combat fake news and disinformation on social media.
Dariusz Prokopowicz ,
I previously did some research on detecting fake news and basically focused on three techniques that are not sentiment based (following Ahmad Al Khraisat last paragraph) that I found very useful. These are:
- User behavior profile- You focus on the agent generating the content and compare the content generated y the actor to a collection of similar data. That is, you establish a metric where a non fake review is more likely to be similar to other true reviews [1]. If the actor publishes true news it will be similar to other news that are true
- Clustering Narrative Text- This approach focuses on comparing distinctive features of the stories and, when a similarity threshold is met, they are placed in one of two groups, deceptive or truthful stories[2]
- Network Propagation-studies the data patterns on how the information is propagated on the network[3]
Regards
References
[1] Conroy, N. K., Rubin, V. L., & Chen, Y. (2015). Automatic deception detection: Methods for finding fake news. Proceedings of the association for information science and technology, 52(1), 1-4.
[2]Rubin, V. L., & Lukoianova, T. (2015). Truth and deception at the rhetorical structure level. Journal of the Association for Information Science and Technology, 66(5), 905-917.
[3] Zhou, X., Cao, J., Jin, Z., Xie, F., Su, Y., Chu, D., ... & Zhang, J. (2015, May). Real-time news cer tification system on sina weibo. In Proceedings of the 24th International Conference on World Wide Web (pp. 983-988).
Umm, I guess with sentiment analysis it is not possible to detect fake news (I could be wrong in understanding your POV). Sentiment analysis can help in understanding the impact of deliberate fake news spread through 100,000 fake avatars. We can understand overall impact on societal emotions on a fundamentally bipolar scale (positive Vs negative).
To track fake news, in simple words, source/origin of the information has to be traced back. Now this would be a highly cooperative task among internet providers, social media, and government agencies that should reveal the oldest time something similar was popped up on the network.
- Badges
- Science method
- Similar topics
- Mathematics
- Statistics
- Statistical Modeling
- Analyses of Variance
- ANOVA