In the process of artificial intelligence development, AI+ all big data seems to be able to increase efficiency and get a good result. As a matter of course, the combination of social media data and AI will have a negative impact.
We are used to expressing emotions, expressing interests and hobbies in social media, and even inadvertently revealing our personal information. As an ordinary person, even if you know that this is an open space, you will not have the impression of data leakage. After all, most of the information display weights of social media are based on the popularity of information, and individual users’ ordinary speeches are difficult to be unfamiliar. People deliberately see.
But with the super power and data analysis capabilities that AI brings, everything becomes different. After crawling data, AI+ social media data can see more than just one person’s reaction, emotion and expression to things, but can see information about a whole group or even a whole ethnic group. The previous series of information disclosures that almost ran through Facebook’s doorway revealed how much of the scattered personal information that AI might have had.
However, social media data is not only dark, but there are already many neurological and psychological studies that have begun to use AI and social media data to use machines to deal with human nature.
“Twitter big data tells you that people around the world are the same”
Recently, the University of Bristol used machine learning to analyze 800 million tweets in 57 cities in the UK within four years and reached a conclusion that we have long pondered about. Humans generally have high emotions in the morning and low emotions in the night.
The whole analysis process is like this. The research team sampled through the Twitter search API and collected 800 million tweets. All the # topics, emojis, holiday greetings, etc. are washed away, and the words are tagged according to the psychometric method.
For example: emotional positive emotions and negative emotions; time-oriented attention now, attention to the past, attention to the future; personal attention to work, family, money, society, religion, and so on.
With this strictly machine-learning model based on the dimensions of psychological research, the research on social media data will be more specialized, rather than simply relying on NLP to analyze the emotions in the language.
The conclusion of the final study is: In the 24 hours of the day, human emotions will not only change, thinking patterns will also change.
From the 5-6 pm of the day, people began to enter the peak period of expression in social media, and at this time people’s emotional expression is more positive, and the focus is also more on the individual status. As time goes by from 7-9, people’s emotions tend to be biased towards anger, but if it is on non-working days, this positive and enjoyable state will continue.
At this time, people’s thinking patterns tend to be class-thinking, thinking is more clear and direct, logical, and stereotyped tendencies appear.
At night, people’s emotional expression will turn negative, and the focus will shift from individuals to society. As time goes on, the closer to the next day’s 3-4 a.m., the more people focus their attention on religion. In this period of time, people’s thinking patterns tend to be existentialism, which reflects the state of confusion, anxiety, irrationality, and willingness to participate and share.
In plain words, one’s general state is to rise in the morning with enthusiasm and self-confidence, to plan his own life in the blood of chickens, and to wait until the evening when he starts to fall into a state of whimper, paying attention to every corner of the world. People who are sad or touched, if they can’t sleep at night, begin to seek religious salvation. Look, is this process the same for Chinese foreigners?
When social media becomes a research assistant in psychology, can it be diagnosed from a selfie?
In fact, the time-cycle changes in human emotions have long been confirmed. Because of the physiological causes of nerve fatigue, melatonin secretion, etc., our emotions will show different states during the day.
Although this study of social media big data just confirmed this change again and did not uncover more reasons for emotional change, it is the first time that the relationship between mood cycles and thinking patterns has changed. In fact, there are many researches on social media data and psychology, and many interesting information have been discovered.
For example, last year, the University of Pittsburgh conducted a survey to investigate the social media use of depression patients. The results showed that the average duration of social media usage for depression patients was much higher than that of ordinary people.
Harvard University research shows that people with depression prefer to use cool, faded, or black-and-white filters when they publish photos on social media.
Unbreakable Ethics Levels
At present, the role of social media data for psychology seems to remain in academic research. Can we see the psychological application of social media data in our lifetime?
At present, social media data has at least the following applications for psychology:
1.As an aid to the measurement of mental state
In addition to those mental illnesses that directly lead to hallucinations, insomnia, and other direct manifestations, there are many types or degrees of mental illness that are difficult to objectively feel. Most of the time, it is necessary to rely on the face-to-face consultation or to fill in the psychological state measurement table to confirm, but the patient may not be able to directly show his or her true state when he or she fills out independently. At this time, information undoubtedly revealed in social media can be used as a support.
2.The psychological status of the group
Compared with the individual’s psychological problems, the more complicated situation is experienced by an entire group. For example: changes in mental state that can occur when disasters or accidents occur.
For example, employee/student suicide occurs in a company or school, or an entire region suffers serious natural disasters such as earthquakes and typhoons. At this time, we often do not have the energy to do psychological counseling for everyone, and there is no way to assess the psychological status of the group as a whole. At most, the group conducts psychological counseling in the form of group lessons.
At this time, using machine learning to research social media data, you can clearly see the group’s psychological response to events. Even the long-term psychological status tracking of the crowd, and selective, targeted psychological counseling.
HIT has proposed a method to identify college students’ social media data by establishing classifiers to identify the risk of depression.
In fact, the application methods mentioned above are hardly technically difficult to achieve. Although the results obtained may not always be absolutely accurate, the value that can be provided for psychology, a labor-intensive industry, is very small.
But the biggest issue is whether it is ethical. Should publicly released social media data be considered personal privacy? The information extracted from it is not considered personal privacy? Even if it is a patient with mental illness, citizens should have the right not to disclose their prevalence, and to discover the citizens’ mental health status through social media data. Is this a serious violation of this power? In particular, if this technology is applied to colleges and universities, will anyone be so concerned that the teachers and classmates around them have learned their psychological state and have made their mental condition worse?
In fact, to a certain extent, we sometimes deliberately choose some ineffective solutions to problems, but we can exchange security and freedom for the soul.