Neural Network Based Attention Degree Prediction for Internet Incidents in One-Crest Period

Sha He, Yuzi Wang, Yue Wang, Qingjie Zhang, Yuejin Zhang, Tianmei Wang School of Information

Central University of Finance and Economics Beijing, China

hesha0987@163.com

Abstract—Observing an Internet incident, we find that its attention degrees develop in multiple wave crests. We propose a basic model to predict the trend of one wave crest based on back propagation (BP) neural network. Simulation experiments show that our model can predict one-crest trend of an Internet incident under the assumption that its maximum attention degree can be estimated. Our work can serve as an auxiliary tool for social or commercial workers to make decisions based on public opinions.

Keywords-public opinions; attention degree; forcasting; Internet incidents; BP neural network

I. INTRODUCTION According to the 26th survey in CNNIC, by the time of

June 2010, the total number of Chinese netizens is up to 420 million with the percentage of 31.8%. Netizens can not only receive information via Internet, but also express feelings. With the advent and prosperity of the Internet, the room of public opinions has been largely widened [1].

Our work can serve as an auxiliary tool for social or commercial workers to make decisions based on public opinions. When an emergency or unexpected event breaks out on the Internet, its transmission rate is absolutely higher than that in traditional media. For marketing, when tendency of an incident can be predicted at the very beginning, it will be beneficial for the marketing managers, as they can take advantage of it to produce an advertisement effect. Even an incident can be created artificially. After a short period of time, the present attention degree of the incident created can be collected. If the future attention degree is predicted to be low or decrease immediately, the incident is considered unsuccessful and need more devotion to enhance its influence. Or, it is considered successful and available for advertisement effect. For social incidents, the increasingly high attention degree characterizes a serious one, which needs quick measures.

Observing an Internet incident, we find that its attention degrees develop in multiple wave crests. In each period, the attention degree rises quickly from very low to the peak, and then gradually drops down. Finally, attention degrees sustain at a low level.

In our work, we propose a basic model to predict the trend of one wave crest based on backpropagation (BP) neural

network. Simulation experiments show that our model can predict one-crest trend of an Internet incident under the assumption that its maximum attention degree can be estimated.

Our paper is organized as follows. In Section II, we introduce the basic knowledge of BP neural network. In Section III, we present our prediction model based on it. In Section IV, we present our experiment settings and evaluate the performance of our model. In Section V, we discuss the limitations. In section VI we present related works. In Section VII, we make acknowledgement. Finally, we conclude our paper in Section VIII.

II. BACKGROUND ON BP NEURAL NETWORK According to the connecting way, neural networks are

usually divided into two categories: feed forward neural network and integrated neural network. Feed forward neural network is consists of one input layer, multiple layers of intermediate hidden layer and one output layer and it does not have a feedback connection, while in the integrated network any two neurons may connect to each other [2]. In this paper we use a well-known model of feed forward neural network, backpropagation (BP) neural network.

BP neural network consists of information forward-propagation process and error back-propagation process. Input layer neurons are responsible for receiving information from the outside world and passing it to the middle layer neurons. The intermediate layer is an internal information processing layer, responsible for information transform. According to the demand of the considered problem, the middle layers and neutrons can be in any number. The information transmits from the last hidden layer to the output layer. The results of information processing are output from the output layer. When the actual output and the expected output do not comply, neural network enters the error back propagation stage. Errors pass through the output layer, amend each layer weights according to the error gradient descent method, and propagate back to hidden layer and the input layer. Propagating the information forward and back again and again, each layer weights adjust ceaselessly. That is the learning process of neural network. This process will be carried out until the network output error is reduced to an acceptable level, or the network achieves a predetermined number of learning.

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III. PREDICTION MODEL Our prediction model will predict attention degrees for one

single-crest period of an incident suppose we know the first n days. Particularly, we use the attention degrees on the first n days to predict that on the (n+1)th day. Then we use the attention degrees on the 2nd to (n+1)th days as input to predict that of the (n+2)th day. Note that in the second step we use the predicted value of the (n+1)th day rather than the real data. The process goes on until the end of the period.

Our network structure is shown in Figure 1

Figure 1: A neural network with a 7-node input layer, a single-node output

layer and 3-node and 7-node hidden layers

A. Input and Output Definition If the number of input nodes is set too small, the prediction

accuracy of the model will be affected. If it is set large, admittedly the predictive accuracy will rise. However, it requires a number of days to get the initial input for prediction and thus we probably miss the best time to take measures. Here we choose 7 input nodes to receive the attention degrees of seven days and 1 output node to output the attention degree of the next day.

B. Nodes of Hidden Layers By experiments we find that the two hidden layers with 3

nodes and 7 nodes generate the best performance.

C. Activation Function The activation function of BP network is a nonlinear,

differentiable non-decreasing function. The general choice is a (0, 1) S-type function, such as f(net)=1/(1+exp(-net)). Because the S-type activation function can handle and approximate nonlinear input/output relationship. However, if the output layer uses S-type function, the output is limited to the area of [0, 1]. If a linear activation function is used instead, network can output any value. So only when the output of the network is restricted, for example, between 0 and 1, the output layer would be allowed to use S-type activation function [3]. We use tan-sigmoidz function and log-sigmoidz function as the activation function of the two hidden layer [4][5] and pure linear function as the output layer activation function. We use gradient descent algorithm as learning training function.

IV. PERFROMANCE EVALUATON

A. Sample Data BP neural network is sensitive to sample noise. If the

sample itself has errors and disturbances, the output of the system would error a lot. Therefore, in considering the diversity and uniformity of the sample, we shall at the same time ensure the accuracy of samples, removing abnormal sample data and preventing lowering the generalization ability of the network [6]. Baidu Index offers free mass data analysis service based on webpage searching. It draws the tendency graph of any keyword over a period of time according to attentions of users and media. Because Baidu users basically can cover all Chinese Internet users, Baidu Index can reflect Internet public opinions authentically and authoritatively. In our experiments, we randomly select 30 incidents as training samples such as "my dad is LiGang", "7.23 rail car accident" and " Yao JiaXin incident” and another 10 incidents as test samples. One thing need to mention is that to collect data conveniently, we use the critical points of an incident to fully restore its line of trend. We use the following linear function to acquire the rest degree of attention. Eq.(1) is the linear function:

Where Xi and Yi respectively denote the day and degree of

attention of day i

B. Data Preprocessing We perform normalization for input data as follows.

, where Xi denotes the attention degree on day i; Xmin and

Xmax denote the smallest and largest attention degrees in the considered period, respectively; Xi’ is the normalized attention degree in the range of [-1, 1] in order to agree with the range of activation functions.

C. Prediction Results After 8 times training on the 32 samples, the mean square

error between the network-inferred outputs and the target outputs is 0.010 and the learning rate is 0.021. Table 1 presents the training samples of 32 incidents. Table 2 presents the testing samples of 8 incidents.

The experimental results show that the predicted tendencies roughly agree with the real ones, as shown in Figure 2. Learning speed and convergence speed are both fast.

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Figure 2: Prediction results and real values of six incidents. (a) is Foxconn employees jump off building event; (b) is the Shanxi vaccine events; (c) is the Zhang Wu of the event; (d) little Yueyue event; (E) is principal support style;

(f) is Yuan Tengfei event. Solid lines are the real trend, the dotted line to predict trends.

Figure 3 shows the mean square error between the predicated values and the real ones for each testing sample.

The average error is 0.2212. The model predicts less accurately in absolute values. The reasons may be deficient training samples and the stochastic nature of an incident’s tendency.

Figure 3: Mean square errors of 8 incidents

V. LIMITATIONS Although our model can predict the rising and falling

tendency, it has some limitations. First, in order to normalize attention degrees, we need to know the minimum/maximum ones which are hard to estimate in prior. One improvement is to add an input node regarding the number of days which is used to predict infection points without relying on the maximum attention degrees. Second, the method using neural network gives deterministic (average) predictions, while the development of an incident is stochastic in nature subject to various uncertain influencing factors. We will consider stochastic models in our future work.

VI. RELATED WORKS In [7], the correlation of understanding properties in the

network public opinion situation was considered, and the authors put forward a network public opinion situation understanding method based on fuzzy integral, and validate the method by an example. But this method can’t make long-term developments trend forecast.

The paper [8] provides a Web public opinion trend forecasting method. Through the period analysis and hierarchical clustering for each type of event, the authors establish a model library for the development trend. When the predictive opinion event occurs, firstly determine the event category and get the time sequence. After the adaptive scaling transformation, they apply the method of least squares, and select incidents which have the least sum of mean square error, in order to predict the development trend in the future. However, the model uses 10 days attention degree to predict the whole trend following, not taking the time series into consideration, which will disturb the forecast accuracy.

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From the angle of quantity, paper [9] collect online news commentary data, then processing and clustering. Its authors summed up the online dissemination rule in different categories of news events. In this paper the fitting curve model can predict the sudden news commentary development trend. But this model only considers the news events and can’t comprehensively reflect the whole network situation.

Paper [10] based neural network theory constructs the analysis system for public opinion tendency in the text of Internet. Through training the weight of text tendency words, the authors get more reasonable text tendency words weight, thereby improving the text tendency judgment accuracy.

We present a simple model to predict the time series of an incident based on Baidu Index.

VII. CONCLUSION Observing an Internet incident, we find that its attention

degrees develop in multiple wave crests. In this paper, we propose a basic model to predict the trend of one wave crest based on backpropagation (BP) neural network. The test results show that the model can accurately predict the overall attention degree tendency. And the mean square errors under least square criterion are acceptable. Our work can serve as an auxiliary tool for social or commercial workers to make decisions based on public opinions.

Further work may solve the problem how to predict the inflection point accurately without relying on the known maximum attention degree. In addition, the model can’t accurately predict the absolute value, which also needs the future work’s improvement.

ACKNOWLEDGMENT We thanks for the supports from Beijing Planning Office

of Philosophy and Social Science (Project No. 12JGA014) and National Natural Science Foundation of China (Project No. 61100112).

REFERENCES [1] Fong J. Burton S.A cross-cultural comparison of electronic word-of-

mouth andcountry-of- origin effect s. ScienceDirect Journal of Business Research. 2008. Vol(61): 233- 242.

[2] Chen Yongmei. Neural network pattern recognition pretreatment methods in handwritten digit recognition [D]. Beijing:Institute of Semiconductors.1995

[3] Wamg Sha. BP neural network in stock prediction. Central South University. Master degree. 2008

[4] Gui Xiancai. BP neural network based on MATLAB and Application [M]. Journal of Zhanjiang Normal University. 2004.Vol.25 N0.3.

[5] Zhang Yanmei, Hu Wenshu, Zeng Xi. Neural network based Chinese word segmentation technology research [J]. Software Guide. 2007(12).

[6] Wu wei, Chen Weiqiang, Liu Bo. Using BP neural network to forecast the stock market [J]. Journal of Dalian University of Technology. 2001. 41(1):9-15

[7] Ding Juling Le Zhongjian Xue Quanquan. Quantitative network public opinion crisis early warning model [J]. Library and information work. 2011(20) No.17.

[8] Gao Hui Wang Shasha, Fu Yan. Web public opinion trend forecasting method. Journal of University of Electronic Science and Technology of China [J]. May 2011. Vol.40 No.3.

[9] Peng Dan, Xu Bo, Song Xianlei. Public opinion research based on network comments [J]. Journal of Modern Information Dec 2009 Vol(29) No 12

[10] She Zhengwei, Qian Songrong. Neural network based text tendency analysis system research. Microcomputer Applications[J] 2011 Vol. 27 No.12

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TABLE I. TRAINING SAMPLE

Incidents Time

Attention degree(sta

rt, max, end)

Incidents Time

Attention degree(sta

rt, max, end)

Incidents Time

Attention degree(sta

rt, max, end)

Incidents Time

Attention degree(sta

rt, max, end)

Tencent& 360

27-Oct-10 484 The Shanghai World Expo

12-Feb-10 2701

Su Zizi

15-Nov-10 63 Xie Tingfeng & Zhang Bozhi's Divorce

23-May-11 613

4-Nov-10 24716 30-Apr-10 166875 10-Jan-11 86293 30-May-11 69941

31-Dec-10 158 29-Nov-10 3048 27-Jun-11 2731 26-Sep-11 434

Guo Degang apprentice beating

1-Aug-10 0 Tang Jun education incident

4-Jul-10 0 Time-travel drama

15-Aug-11 951 Earthquack in Japan

13-Mar-11 0

2-Aug-10 754 12-Jul-10 1056 12-Sep-11 1821 14-Mar-11 251930

25-Oct-10 56 4-Jul-11 108 26-Dec-11 880 22-Aug-11 1412

My dad is Ligang

17-Oct-10 0 Fang Zhouzi vs.Han Han

28-Jan-12 0 Wenzhou rail car accident

22-Jul-11 0 Naked marriage

25-Apr-11 2550

18-Oct-10 113605 1-Feb-12 1416 25-Jul-11 15667 20-Jun-11 100882

30-Nov-11 829 29-Apr-12 114 14-Nov-11 736 14-Nov-11 2625

Cao Cao Tomb

2-Dec-09 0 Google Sign out Chinese market

10-Jan-10 0 The situation in Libya

14-Feb-11 0 The new marriage law

1-Aug-11 781

18-Dec-09 743 11-Jan-10 24662 21-Feb-11 19409 15-Aug-11 88068

7-Mar-11 85 26-Jul-10 154 4-Jul-11 611 2-Jan-12 1570

The Wangjialing coal mine

28-Mar-10 0 The girl we loved those years

19-Oct-11 1588 Lai Changxing

27-Jun-11 1864 The ninetieth anniversary of the CPC founding

27-Feb-11 0

5-Apr-10 4033 4-Dec-11 61510 18-Jul-11 123299 27-Jun-11 1957

31-Jan-11 95 21-Mar-12 6604 24-Oct-11 2221 27-Feb-12 155

Tang Fuzhen

29-Nov-09 0

Guo Meimei

21-Jun-11 2126 Beijing-Shanghai high-speed railway

14-Mar-11 1060 The death of Kim Jong Il

12-Dec-11 318

30-Nov-09 5079 28-Jun-11 702968 27-Jun-11 26556 19-Dec-11 102587

5-Jul-10 135 4-Oct-11 6585 31-Oct-11 889 30-Jan-12 277

The death of Jobs

10-Sep-11 1205

Liu Yan

16-May-11 3598 Tainted steamed buns

10-Apr-11 0 Startling step by step

1-Aug-11 22123

6-Oct-11 254388 12-Oct-11 69722 11-Apr-11 7340 12-Sep-11 1634571

30-Oct-11 773 14-May-12 13416 1-Aug-11 143 19-Dec-11 39303

Song Zude

13-Oct-08 854 Deng Jianguo's marriage

3-Oct-11 0

27-Oct-08 23753 17-Oct-11 4979

27-Jul-09 2374 6-Feb-12 70

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TABLE II. TESTING SAMPLES

Incidents Time

User attention

(start, max, end)

Incidents Time

User attention

(start, max, end)

Foxconn jump off building event

17-Jul-11 394

Problems in vaccine

21-Mar-10 0

20-Jul-11 7611 29-Mar-10 464

24-Sep-11 507 8-Nov-10 44

Yuan Tengfei

26-Apr-10 0

Zhang Wuben

30-May-10 0

10-May-10 12747 31-May-10 2044

10-Oct-10 992 28-Mar-11 195

Demolition

12-Sep-10 0

Little Yueyue

14-Oct-11 72

13-Sep-10 333 21-Oct-11 327868

21-Mar-11 44 15-Dec-11 1646

Five-stroke armband juvenile

1-May-11 0

The principal support style

18-Oct-11 0

2-May-11 25014 20-Oct-11 8857

18-Jul-11 325 3-Dec-11 173

Little Yueyue

27-Sep-10 0

Tiangong-1

29-Aug-11 0

11-Oct-10 489286 26-Sep-11 251930

11-Apr-11 4058 12-Dec-11 1412

158