3.1.1 Detecting Influential Transmitters

Influential transmitters are different from influential users. The extraction method of influential authors ⁵ used the most influential user by retweets, mentions and replies among all the users. However, influential transmitters to be extracted in this research means users who play an important role of spreading of the followee’s tweets or opinions in order to support the followee.

Since the conventional HITS algorithm can only detect influential users, we revised an edge on the HITS graph to delicately express the relationship of retweeting by applying the concept of social contagion to detect influential transmitters. The social contagion refers to tendency for certain behavior exhibited by one user to be copied by other users who have been exposed to media coverage describing the behavior of the target user¹².

When a follower retweets a tweet of a target user, an edge is connected between the follower and only to the transmitter, not the original user of a tweet. The connecting condition of an edge is as follows.

The authority and hub score are calculated as follows on HITS graph:

where w _ij is for a weight of e _ij and e _ij is the number of followees who retweet a tweet of j before i retweets when i retweets an j’s tweet.

When the conditions as described above are satisfied, the user A gives an effect to the user B when B retweets a tweet of T. Therefore, the related work ⁵ creates edge of A->T and B->T, the proposed method, however, creates an edge of B->A so as to reflect the transmitter. By reflecting these flows of retweeting relationships on the HITS algorithm, the high ranked followers with high authority scores are detected as influential transmitters. Figure 4 shows the results of detecting influential transmitters.

Fig. 2 Modified HITS according to retweet sequence to detect influential transmitters 

Fig 3 Social contagion by influential transmitters. The red colored bar represents that more than ten users retweet amcng 16 users 

Fig. 4 Detecting Influential transmitters 

3.1.2 Follower Polarity Classification

Influential transmitters deliver tweets of a target user to their followers and propagate an opinion of a target user. They can be considered as strong supporters with the same opinion of the target user. Not only retweeting action for a target user’s tweet can be seen as the agreement of opinion of the target user, but also retweeting for an influential transmitter’s tweet can be considered as the supporting action for the target user’s opinion.

Based on the retweet relationships among the followers on social network, the supporters for the target user are classified with strong confidence.

Then, the followers who cannot be classified by the retweet structure are classified by using support vector machines (SVMs) which show high performance in classification. We used bigram and trigram syllables to identify positive or negative sentiments in tweets by constructing bi-gram syllables sentiment dictionary after Korean morphological analysis. The number of positive and syllables and negative syllables in the dictionary are 146 and 135, respectively.

3.2.1 Clustering Tweets on Social Issues Based on LDA

Latent Dirichlet Allocation (LDA) is a fully generative model for describing the latent topics of documents and a standard tool in topic modeling ⁸,⁹,¹⁰.

For the extraction method of topics by LDA in social data, TimeUserLDA model ¹¹ is to extract topic which is explosively increased within particular time in tweet data. The TimeUserLDA is adapted for the our work to make issue clusters on each social issue for the followers’ tweets of the target user, by reflecting relations between tweet’s lifetime and target user to topic model.

In order to make clusters of tweets related to a target user’s tweet, two conditions are considered whether it belongs to the lifetime of a target user’s tweet or not. The tweet lifetime of a target user is defined for the time from the creation of a tweet to the time of a final retweet.

Variables indicated in the figure are as follows.

α: Early prior probability each document belongs to topic K

β: Early prior probability each word belongs to topic K

T: Time t ∈ {1,…,T}

F: Set of users writing at time t, f ∈ {1,…,F}

D: All of tweet user u writes at time t, d ∈ {1,…,M}

W: Total words appeared in document d, w ∈ {1,…,N}

η ^f : probability of topic k appeared in user f, f ∈ {1,…,F}, k ∈ {1,…,K}

θ ^t : probability of topic k appeared within the lifespan of a tweet of a target user t∈{1,…,T}, k∈{1,…,K}

φ ^c : probability of word w belongs to topic k, k ∈ {1,…,K}, w ∈ {1,…,V}

z: Topic ratio of word w in document d, d ∈ {1,…,M}, w ∈ {1,…,N}

K: the number of topic k ∈ {1,…,K}

π: Probability of selecting a topic of a target user rather than individual topic

A tweet posted by a follower from the time of a tweet created until the last retweet probability can be associated with the target user's tweet.

Follower Opinion classification for Each Issue. The clustered tweets on each issue are analyzed for sentiment whether to express positive, negative or neutral opinion toward the opinion of a target user. Here, retweeting activities for a tweet can be considered as a positive opinion toward the target user for the issue. Based on retweeting relationship, tweets are classified as positive.

For the analysis of other tweets, we used SVMs to classify a follower’s opinion based on the bigram and trigram syllables in a sentiment dictionary for Korean characters to consider a user’s writing patterns by using a variation of a word on Twitter.

4.2.1. Results of Follower Polarity Classification

Comparison methods for a follower’s polarity classification are as follows.

Table 3 shows results of follower polarity classification. The classification with influential transmitters achieves higher performance compared to the related work ⁵.

Table 3 Experimental results of follower bias classification 

Table 4 shows the results of the classification for each class in precision. The proposed method in the supporting follower detection shows higher performance as compared to the comparative method. It shows that the method using influential transmitters is effective to classify the supporting followers.

Table 4 Results of each class (in Precision) 

4.2.2. Results of Social Issue Clustering

Comparison methods for tweet clustering are as follows.

Table 5 shows the result of the social issue clustering. The 200 tweets are randomly selected from each cluster which is judged to be an issue for a target user among the clusters. The experimental result shows that the model using the lifespan of tweets is effective.

Table 5 Experimental results of tweet clustering 

4.2.3. Results of Opinion Classification of a Follower

Comparison methods for classifying opinions are as follows.

Table 6 shows the results of opinion classification. This result shows that newly-coined words and the variation of words by users on social network can be considered by using trigram syllables.

Table 6 Experimental results of opinión classification 

4.2.4. Analysis of Opinions for Followers

Even though a follower supports a target user, the follower can have an opposite opinion toward the target user. On each social issue of the target user, the supporting and non-supporting ratio is analyzed.

Table 7 shows the results of follower polarity and opinion classification for tweets on each issue for the target user. According to opinion classification for the particular issues by each target user, results of opinion classification exist users who have opposing opinions unlike the followers classification. This analysis shows that a follower is not always supporting toward the target user. A target user can take an advice by the followers’ responses for the future action.

Table 7 Results of follower classification and opinion classification on each issue for the target user