Prediction of information epidemics by analyzing the social graph
At some point, the safe haven of the Internet I perceived stirred one of the TED lectures.
Description of the lecture from the site:
Attention.
To understand the following text, the lecture is required.
It lasts only 18 minutes and has Russian subtitles (thanks to Nadezhda Lebedeva ).
The possibility of predicting the behavior of network actors solely by analyzing the distribution of content turned my head around! By no means did I decide to repeat the experiment at least in artisanal conditions. Having cooled my head and thought it over, I had two complaints against professors:
1) The behavior of biological viruses (influenza, T-Virus), if it can be predicted by propagation statistics, then with a huge error, because in addition to the actors' contacts, many third-party factors will also affect distribution, from the physical health of the participating characters to flares in the sun.
2) The concept of the “Paradox of Friendship” (a random person calls his friend and in most cases will have more friends) sometimes it seems to me to be absurd at all. To check, I asked ten of my friends to name at random some of my friends, and the choice of only two of them was in solidarity with the “Paradox of Friendship” theory.
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To solve the first question, I selected a request for help as an information virus. One of the participants in the sample hijacked a scooter. This information guide was convenient because the victim conducted searches using social networks and regularly reported back to them about the results.
To solve the second question instead of the “Paradox of Friendship” theory, in order to identify the most popular users in the sample, I simply counted the number of links of each participant within the sample.
It is also necessary to mention that, unlike the original research, the social network VKontakte was chosen as the medium.
The sample was collected from 100 students from different faculties of one Moscow university.
The time scale is divided into 20 marks, where one mark is one day (the period from July 30 to August 18).
By “Moment of infection” is meant the first contact of the user with the information guide (repost, commenting on the record, etc.).
All data collection and processing took place manually (I am a humanist).
The result of the dreary calculations was the table below.
(three most popular and non-popular actor shown)
Based on the data of the popularity of the actors within the sample, I divided them into two groups:
Group A - the most popular 20 users
Group B - the remaining 80 users
And the final result of the work was the schedule.
In spite of the fact that this research of mine confirmed the original theory, it is necessary to remind once again that the experiment was conducted under conditions of artisanal, and therefore it cannot be relied on too much. But I myself firmly believed in the possibility of predicting information viruses using similar methods.
I would be grateful for the advice and recommendations.
Description of the lecture from the site:
After mapping intricate social networks, Nicholas Kristakis and his colleague James Fowler explored the possibilities of using this information for good. And now Nicholas Kristakis will unveil his latest discovery : social networks can be used as the fastest method for detecting the spread of any epidemics: from innovative ideas to socially dangerous behavior or viruses.
Attention.
To understand the following text, the lecture is required.
It lasts only 18 minutes and has Russian subtitles (thanks to Nadezhda Lebedeva ).
The possibility of predicting the behavior of network actors solely by analyzing the distribution of content turned my head around! By no means did I decide to repeat the experiment at least in artisanal conditions. Having cooled my head and thought it over, I had two complaints against professors:
1) The behavior of biological viruses (influenza, T-Virus), if it can be predicted by propagation statistics, then with a huge error, because in addition to the actors' contacts, many third-party factors will also affect distribution, from the physical health of the participating characters to flares in the sun.
2) The concept of the “Paradox of Friendship” (a random person calls his friend and in most cases will have more friends) sometimes it seems to me to be absurd at all. To check, I asked ten of my friends to name at random some of my friends, and the choice of only two of them was in solidarity with the “Paradox of Friendship” theory.
')
To solve the first question, I selected a request for help as an information virus. One of the participants in the sample hijacked a scooter. This information guide was convenient because the victim conducted searches using social networks and regularly reported back to them about the results.
To solve the second question instead of the “Paradox of Friendship” theory, in order to identify the most popular users in the sample, I simply counted the number of links of each participant within the sample.
It is also necessary to mention that, unlike the original research, the social network VKontakte was chosen as the medium.
The sample was collected from 100 students from different faculties of one Moscow university.
The time scale is divided into 20 marks, where one mark is one day (the period from July 30 to August 18).
By “Moment of infection” is meant the first contact of the user with the information guide (repost, commenting on the record, etc.).
All data collection and processing took place manually (I am a humanist).
The result of the dreary calculations was the table below.
(three most popular and non-popular actor shown)
Based on the data of the popularity of the actors within the sample, I divided them into two groups:
Group A - the most popular 20 users
Group B - the remaining 80 users
And the final result of the work was the schedule.
In spite of the fact that this research of mine confirmed the original theory, it is necessary to remind once again that the experiment was conducted under conditions of artisanal, and therefore it cannot be relied on too much. But I myself firmly believed in the possibility of predicting information viruses using similar methods.
I would be grateful for the advice and recommendations.
Source: https://habr.com/ru/post/165067/
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