CN109472415B - Method for predicting event scale in social media through dynamic characteristics - Google Patents
Method for predicting event scale in social media through dynamic characteristics Download PDFInfo
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- CN109472415B CN109472415B CN201811359804.5A CN201811359804A CN109472415B CN 109472415 B CN109472415 B CN 109472415B CN 201811359804 A CN201811359804 A CN 201811359804A CN 109472415 B CN109472415 B CN 109472415B
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Abstract
The invention discloses a method for predicting the event scale in social media through dynamic characteristics, which comprises the following steps: (1) unsupervised combination is carried out on social media content to generate an event set, the heat of each event in the event set is calculated, and finally a basic heat event meeting a basic heat threshold is extracted; (2) and if the heat of the basic heat event is in the acceleration process, predicting whether the basic heat event is possible to become a large-scale event or not and the arrival time of the large-scale event possibly through the result of variable-speed motion. The invention maps the development of the event into the variable-speed motion, takes the possible development scale of the event as the expectation, calculates the possibility and the arrival time, can carry out more direct and effective real-time measurement and calculation on the scale of the event, and has higher efficiency and more accurate precision.
Description
Technical Field
The invention relates to the technical field of information, in particular to a method for predicting the scale of an event in social media through dynamic characteristics.
Background
With the rapid and high-speed development of the mobile internet, the era that people are self-media is entered, the interaction is more real-time and convenient, and the cost is lower. The rapid and wide spread of events on the network or in reality through the social media platform gradually deepens the influence on social life, policy and regulation and the mind of people. The problems of event monitoring, public opinion guidance and the like cause high attention of all levels of government and enterprises and public institutions. Therefore, real-time monitoring of social media, especially prediction of future development of "risk potential" events, is the most important reference in event monitoring, public opinion guidance work.
At present, the existing event scale prediction is based on the summarization and classification of a large number of events, and experiences and models are formed mainly from the aspects of an event diffusion mode and an information transmission mode and are used for the event scale prediction. Wherein, the event diffusion mode proposes emergent and local diffusion, depth spreading diffusion, area displacement diffusion, heterogeneous conversion diffusion, linkage diffusion, circulating diffusion, radiation diffusion and the like; as the information transfer method, single-chain transfer, tree transfer, mesh transfer, and the like are proposed.
But with the increasing speed of the internet, the possibility of spreading and spreading new types and new regular events exists. At this time, according to the conventional method of accumulation and summary, since it needs more time for accumulation of emerging events for correlation analysis and summary, there is a lag period inevitably, and such a real space interval is likely to affect the judgment of event development, miss the best opportunity of public opinion guidance, lead to natural and malignant development of events, and affect the normal and stable order of society.
Therefore, a new method which does not depend on a supervised learning process, has no lag phase and can highly adapt to the development rules of various events in the past, the present and the future is needed.
Disclosure of Invention
The invention aims to solve the problems in the prior art and provides a method for predicting the event scale in the social media through dynamic characteristics.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a method for predicting the size of an event in social media by means of dynamics, characterized in that it comprises the following steps:
(1) unsupervised combination is carried out on social media content to generate an event set, the heat of each event in the event set is calculated, and finally a basic heat event meeting a basic heat threshold is extracted; wherein, the heat of the event is obtained by weighted and comprehensive calculation of the reading number, the comment number and the forwarding number;
(2) and if the heat of the basic heat event is in the acceleration process, predicting whether the basic heat event is possible to become a large-scale event or not and the arrival time of the large-scale event possibly through the result of variable-speed motion.
The unsupervised merging in the step (1) refers to: the method comprises the steps of segmenting social media content, extracting characteristic words through TF-IDF, judging similar content when two identical characteristic words are larger than or equal to two identical characteristic words, and automatically combining the similar content.
The variable period sampling in the step (2) refers to: and continuously and periodically sampling the heat of the basic heat event, and adjusting the length of the sampling period according to the change speed of the heat of the basic heat event during sampling.
The heat degree of the event in the step (1) is calculated by a heat degree calculation formula, wherein the heat degree calculation formula comprises the following steps: r ═ p β + q × + z × + δ + s ∈; wherein p represents a number of reviews, β represents a review factor, q represents a reading number, γ represents a reading factor, z represents a forwarding number, δ represents a forwarding factor, s represents a number of constituent events, and ε represents an event factor;
after the heat of the basic heat event is sampled in the step (2) in a variable period, a set of heat values is obtained as r ═ { r1, r2, r3, r4,. rn }, a corresponding set of sampling time points is t ═ { t1, t2, t3, t4,. tn }, and a speed calculation formula is firstly obtained according to the set of heat values r and the set of time points t
Sum acceleration calculation formula
Respectively calculating a speed set v ═ { v1, v2, v3, v 4.. vn } and an acceleration set a ═ a1, a2, a3, a 4.. an }; and respectively calculating the unequal weight average values V & ltV & gt 1, V2, V3, V4, & gt.Vn & ltA & gt 1, A2, A3, A4, & gt.an & ltA & gt } according to the following formula every 5 continuous periods,
unequal weight means V equation:
unequal weight average value a formula:
the sampling period T ═ T of the variable period sampling1,T2,T3,T4,...TnThe formula of the sampling period is: t _ default/(1+ (V-T _ Thd)/T _ Thd), wherein T _ default is a constant and represents a minimum period value; t _ Thd is a constant, representing the maximum threshold value of V;
finally, whether the basic heat event is possible to become a large-scale event and the arrival time of the large-scale event is calculated and predicted according to the following prediction formula,
the prediction formula is as follows:
where P is a constant representing the scale expected value of the base heat event, and t is the arrival time at which it is possible to become a large-scale event.
The invention has the advantages that:
1. according to the invention, the event development is mapped into variable-speed motion, the possible development scale of the event is predicted by the dynamic characteristics, the possibility and the arrival time are calculated, the event scale can be directly and effectively measured and calculated in real time, the efficiency is higher, and the precision is more accurate. Furthermore, the invention can predict the scale of the event more accurately in real time in advance, so that the development of the event can be judged in advance, the benign development of the event can be guided in the best public opinion guide period, and the malignant development of the event can be effectively prevented.
2. The invention does not depend on a supervised learning process, has no lag phase, and can be highly suitable for a new method of development rules of various events in the past, the present and the future.
3. The invention has full-automatic movement, does not need human intervention, reduces labor and cost, continuously collects data from the Internet every day, extracts basic heat events through information merging and clustering, samples through an intelligent variable period, and calculates early warning possibility and time.
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Detailed Description
The invention discloses a method for predicting the event scale in social media through dynamic characteristics, wherein the used original data is a content set propagated by the social media, the text data comprises texts and comments, and the numerical data comprises reading numbers, forwarding numbers and comment numbers, and the method specifically comprises the following steps:
(1) unsupervised combination is carried out on social media content to generate an event set, the heat of each event in the event set is calculated, and finally a basic heat event meeting a basic heat threshold is extracted; wherein, the heat of the event is obtained by weighted and comprehensive calculation of the reading number, the comment number and the forwarding number;
(2) and if the heat of the basic heat event is in the acceleration process, predicting whether the basic heat event is possible to become a large-scale event or not and the arrival time of the large-scale event possibly through the result of variable-speed motion.
The unsupervised merging in the step (1) refers to: the method comprises the steps of segmenting social media content, extracting characteristic words through TF-IDF, judging similar content when two identical characteristic words are larger than or equal to two identical characteristic words, and automatically combining the similar content.
The variable period sampling in the step (2) refers to: and continuously and periodically sampling the heat of the basic heat event, and adjusting the length of the sampling period according to the change speed of the heat of the basic heat event during sampling.
The heat degree of the event in the step (1) is calculated by a heat degree calculation formula, wherein the heat degree calculation formula comprises the following steps: r ═ p β + q × + z × + δ + s ∈; wherein p represents a number of reviews, β represents a review factor, q represents a reading number, γ represents a reading factor, z represents a forwarding number, δ represents a forwarding factor, s represents a number of constituent events, and ε represents an event factor;
after the heat of the base heat event is sampled in the step (2) in a variable period, a set of heat values is obtained, where r is { r1, r2, r3, r4,. rn }, and a corresponding set of sampling time points is t { t1, t2, t3, t4,. tn }, and through the set of heat values r and the set of time points t, whether the base heat event is likely to become a large-scale event or not and the arrival time of the large-scale event can be predicted according to the following process.
First, according to the velocity calculation formula
Sum acceleration calculation formula
A set of speeds v ═ { v1, v2, v3, v 4.. vn } and a set of accelerations a ═ a1, a2, a3, a 4.. an } of the thermal change are calculated, respectively.
Secondly, calculating unequal weight means V ═ V1, V2, V3, V4,. Vn } and A ═ A1, A2, A3, A4,. An } for each 5 continuous periods according to the following formula respectively,
unequal weight means V equation:
unequal weight average value a formula:
since the development of events is continuous, and the periodic sampling points can only represent the transient state at the moment, the historical trend needs to be combined so as to better reflect the current objective actual situation. Therefore, the specific implementation can use 5 periodic sampling values to carry out unequal weight average processing.
The sampling period T ═ T of the variable period sampling1,T2,T3,T4,...TnThe formula of the sampling period is: t _ default/(1+ (V-T _ Thd)/T _ Thd), wherein T _ default is a constant and represents a minimum period value; t _ Thd is a constant, representing the maximum threshold value of V. Wherein, when the event is developing and changing rapidly, shortening is adoptedSampling period for denser sampling variation; conversely, when the event evolves more slowly, the sampling period may be extended. The variable period has the advantage of being more economical and efficient than the conventional fixed period sampling method
Finally, whether the basic heat event is possible to become a large-scale event and the arrival time of the large-scale event is calculated and predicted according to the following prediction formula,
the prediction formula is as follows:
where P is a constant representing the scale expected value of the base heat event, and t is the arrival time at which it is possible to become a large-scale event.
The following steps are specifically described by adopting a lovely cat in a certain factory as a 'official employee' and being concerned by the network, and specifically include the following steps:
(1) through word segmentation and keyword extraction, the following 4 pieces of information are found out, and event keywords are generated based on the following information: wandering, kittens, concierge, thighs, popularity, excellence, mice;
a special staff is provided, which not only cleans the rat in the factory, but also becomes a special moderator in the factory. Holding the above results, the factory owner may make a special lot of the job to "regular staff" to enjoy the "same level" of welfare meeting! Paste the photo of cat under staff's photo wall.
And b, recently, a wandering cat is caught in a net red in a certain factory due to excellence in mousing and selling, and the small cat rushes to hold the thigh of the other side when people in the mediation room fire and make loud sound each time.
And c, loading the staff manual successfully by a cat star person 'Xiaohuang' in a certain factory shortly before. Formerly, little yellow is merely a wave to be mew, because too many mice in the archives in the factory, the staff just take the mice back to catch the mice. The rat-catching ability is not only strong, but also the adjusting ability is more important.
d, the cat yellow is the genuine people of a certain factory, because wandering cats grab the mouse without authorization and rely on the thigh to sell and mediate disputes. Originally, it is a wandering wave, which is good at catching mice and selling sprouts, and is a place for oneself in the factory. The factory leader introduces that the yellow wine is mainly responsible for catching the mice to protect the files to be safe, the yellow wine can be reached in a kitchen by the yellow wine at the weekend, and the mice are not eaten after being caught. When people open the door all around, the mouse can be seen to be caught by the small yellow on the doorway and the like, and the mouse is placed beside the door for two days. In addition, Xiaohuang is good at holding the thigh to relieve the atmosphere of mediation. Once a person has a fire and a loud sound, the person rushes into the cup to hold the thigh of the other person, and the person who regenerates the qi also eliminates the qi through the holding. Since its predecessor, the work efficiency of factory mediation is obviously improved. In a kitchen, people lie on the table bottom every time eating the kitchen and eat fish heads and leftovers. Staff in the factory can actively bring cat food to the Xiaohuang, and other unit staff can also enjoy the name of the Xiaohuang to bring the Xiaohuang to the cat food.
And then the heat degree of the event is calculated to be 654.0 by a heat degree calculation formula (r ═ p ^ beta + q ^ gamma + z ^ delta + s ∈), and the heat degree of the event meets a basic heat degree threshold, so the heat degree is extracted as a basic heat degree event.
(2) And carrying out variable period sampling on the basic heat event, calculating the speed and the acceleration of the basic heat event through a variable speed motion rule after sampling, and if the heat of the basic heat event is in an acceleration process, predicting whether the basic heat event is possible to become a large-scale event or not and the arrival time of the large-scale event through a variable speed motion result. In particular, see the following table:
from the above table, it can be seen that:
1, the sequence numbers 1-4 are initial sampling stages, and sampling is carried out according to the highest small period.
2, serial numbers 5-10 are the peak period of the Internet, but the rest time is gradually entered, the speed is gradually reduced, and the predicted residual time t is prolonged.
3, serial numbers 11-13 are from late night to early morning, at the moment, the network activity is low, and the predicted residual time t cannot be reached.
4, the serial numbers 14 to 19 are the second day of the incident, the incident is continuously diffused and spread in the network, the predicted residual time t is gradually shortened, and when the predicted residual time t is 0, the large-scale threshold is triggered.
The invention maps the development of the event into variable-speed motion, predicts the possible development scale of the event by the dynamic characteristics, calculates the possibility and the arrival time, can carry out more direct and effective real-time measurement and calculation on the scale of the event in advance, makes judgment on the development of the event in advance, is favorable for guiding the benign development of the event in the best public opinion guide period, and effectively prevents the malignant development of the event.
Claims (3)
1. A method for predicting the size of an event in social media by means of dynamics, characterized in that it comprises the following steps:
(1) unsupervised combination is carried out on social media content to generate an event set, the heat of each event in the event set is calculated, and finally a basic heat event meeting a basic heat threshold is extracted; wherein, the heat of the event is obtained by weighted and comprehensive calculation of the reading number, the comment number and the forwarding number;
(2) the method comprises the steps that the heat degree of a basic heat degree event is sampled in a variable period, the heat degree of the basic heat degree event is calculated to be in an acceleration process or a deceleration process through a variable speed motion rule after sampling, and if the heat degree of the basic heat degree event is in the acceleration process, whether the basic heat degree event is possible to become a large-scale event or not and the arrival time of the large-scale event is possible to become the large-scale event is predicted through a variable speed motion result;
the heat degree of the event in the step (1) is calculated by a heat degree calculation formula, wherein the heat degree calculation formula comprises the following steps: r ═ p β + q × + z × + δ + s ∈; wherein p represents a number of reviews, β represents a review factor, q represents a reading number, γ represents a reading factor, z represents a forwarding number, δ represents a forwarding factor, s represents a number of constituent events, and ε represents an event factor;
after the heat of the basic heat event is sampled in the step (2) in a variable period, a set of heat values is obtained, where r is { r1, r2, r3, r4,. rn }, and a corresponding set of sampling time points is t { t1, t2, t3, t4,. tn }, and the set of heat values r and the set of time points are used for collecting rt, firstly calculating formula according to speed
Sum acceleration calculation formula
Respectively calculating a speed set v ═ { v1, v2, v3, v 4.. vn } and an acceleration set a ═ a1, a2, a3, a 4.. an }; and respectively calculating the unequal weight average values V & ltV & gt 1, V2, V3, V4, & gt.Vn & ltA & gt 1, A2, A3, A4, & gt.an & ltA & gt } according to the following formula every 5 continuous periods,
unequal weight means V equation:
unequal weight average value a formula:
the sampling period T ═ T of the variable period sampling1,T2,T3,T4,...TnThe formula of the sampling period is: t _ default/(1+ (V-T _ Thd)/T _ Thd), wherein T _ default is a constant and represents a minimum period value; t _ Thd is a constant, representing the maximum threshold value of V;
finally, whether the basic heat event is possible to become a large-scale event and the arrival time of the large-scale event is calculated and predicted according to the following prediction formula,
the prediction formula is as follows:
where P is a constant representing the scale expected value of the base heat event, and t is the arrival time at which it is possible to become a large-scale event.
2. A method of predicting the size of events in social media by dynamics in accordance with claim 1, wherein: the unsupervised merging in the step (1) refers to: the method comprises the steps of segmenting social media content, extracting characteristic words through TF-IDF, judging similar content when two identical characteristic words are larger than or equal to two identical characteristic words, and automatically combining the similar content.
3. A method of predicting the size of events in social media by dynamics in accordance with claim 1, wherein: the variable period sampling in the step (2) refers to: and continuously and periodically sampling the heat of the basic heat event, and adjusting the length of the sampling period according to the change speed of the heat of the basic heat event during sampling.
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