visualization of spread of topic words on twitter using stream graphs and relational graphs keigo...

Visualization of Spread of Topic Words on Twitter using Stream Graphs and Relational GraphsKeigo Amma, Shunsuke Wada, Kanto Nakayama, Yuki Akamatsu, Yuichi Yaguchi, and Keitaro Naruse

School of Computer Science and Engineering

University of Aizu, Japan

SCIS & ISIS, JapanIEEE 2014

• Microblog services can post small messages (up to 150 characters). These services are easy to update and users can communicate with other connected users in real time.

• Users can publish and share their moods and opinions.

• This research proposes ways to visualize a set of tweets for analysis of the topic space and temporal patterns and to study how topics and user interests spread or moods change over time.

IntroductionRelated Work

Method

Visualization of Spread of Topic Words on Twitter using Stream Graphs and Relational Graphs

Experiment Result Conclusion

• Yang et al created a visualization scheme for four phases in crisis response (preparation, response, restoration, and risk reduction) using machine learning techniques from a set of tweets.

• Lee et al introduces stream graphs which we also use, but our research aims to indicate how each topic is distributed and how active it is at a particular time; we are not restricted to typhoon disaster tweets.

• Sakaki et al also studied a set of tweets as a sensor. They estimated the spreading of tweets as away of measuring a disaster using supervised machine learning techniques. This research was also an application of information visualization.


Method



• We developed an interface using the Data-Driven Document D3.js, which is a member of the library of JavaScript HTML and JavaScript.


Method



• This figure shows a graph visualizing a change in the use frequency of some words. This figure expresses data by the increase and decrease of the width of each layer. The layers are separated by color; each layer represents the frequency of a single noun.

A. Visualization

• This figure shows a graph visualizing the association of nouns. This graph represents the relevance of words using nodes developed in a particular time frame. Each node corresponds to one noun in tweets.


Method



• Smaller distances between nodes indicate stronger relevance of the nouns. For convenience in calculating the coordinates of nodes, the co-occurrence* of the most important word was not placed at the origin of the coordinates.

*co-occurrence: the two associated topic words occur in the same tweet

• For visualizing the stream graph, we count the noun frequency of the associated topic word of a set of tweets of each user.

• If we have N words, Byron’s graph is defined as follows:

fi,…,fn

• Where the number of words is n(n ≤ N - 1).

• First, the baseline g0 of the stream graph is defined as:

g0 = 0


Method



B. Occurrence frequency of nouns

• Then, if the graph is to be stacked symmetrically, the baseline g0 can be defined as:

g0 = -

• If the graph is to be stacked asymmetrically, then g0 is defined as:

g0 = -

• Here, the top position of the stacked graph gi is given by the ith value of fi, where the time i is such that:

gi = g0 +

• With this information, the stacked graph is drawn as a time sequence.

• We define a topic word and extract associated topic words automatically using semantics, and then we plot the frequency of associated topic words as a stream graph.


Method



• Dbpedia uses Wikipedia as a structured database.

• We use Dbedia to find semantically associated words from topic words automatically.

• For instance, we search using ‘sports’, then we find 52 associated nouns.

• In this research, we visualize the frequency of these associated topic words as a stream graph.


Method



C. DBpedia

• We also plot the association of topic words in a set of tweets. First, we create a co-occurrence matrix S of associated topic words.

• We set period for collection of tweets as one week, then we construct the co-occurrence matrix, and we apply quantification method IV (Q-IV) to calculating the two-dimensional position of each word.

• If two objects have strong associations, then they are plotted closer together.


Method



D. Hayashi’s quantification method type IV

• We collected 836.030 tweets as a seed from user ‘yagu1’ to 300 followers over 104 weeks from May 5, 2010 to May 1, 2012. This dataset contains topics discussed between people.

• First, we applied morphological analysis to the collected tweets. Next, we selected topic words to visualize trends. We selected two topics words ‘ 野菜’ (vegetable in Japanese) and ’ スポーツの’ (sports).

• Then, we found associated words using Dbpedia for each topic word: the word ‘vegetable’ was associated with 72 words, such as ‘ 農家’ (farmer); sports was associated with 52 words, such as ‘ 水泳’ (swimming).

• From this, we counted a set of tweets that were segmented by time, created frequency indexes of these words by time segment, and then plotted them as a stream graph


Method



• The stream graph explains the variation of each associated topic word, and the width of the stream graph shows the variations in numbers of topic words. In figure 3 and 4, each set of tweets includes a weekly view (blue vertical ribbon), and 7-8 weeks in a view. The relational graph supports the week of tweets indicated by the blue vertical ribbon.


Method



• The large red area indicates ‘vegetable’ and its frequency is very high over three weeks. Here, the sentence tweeted multiple times included only the term ‘vegetable’.

• Although this result is a little confused, there are two clusters: {‘vegetable’, ‘salad’, ‘plant’, ‘custom’, ‘farmer’, ‘J-Pepper’} and {‘fruit’, ’melon’, ‘gardening’}. These are well separated from the concepts of ‘vegetable’ and ‘fruit’.


Method



• Comparing existing research, our research is more focusing a short time of particular. The reason that graphs have relational graph. So we can see short point of relationships.

• It is considered that those characteristics is easily out because those associated words selected by Dbpedia. For verification, we need to check more test cases.

• It focused on nouns and eliminated synonyms and abbreviations. We should develop this system further to cover other words such as verbs and adverbs, index synonyms, and abbreviation using Word Net.

• This system is not automatic so we need to analyze individually. We plan to make caches or eliminate data during the calculation process for large-scale tweet databases to reduce processing time.

• In addition, we plan to automate the entire process from gathering Twitter data to visualization and to publish this service.


Method



Visualization in Media Big Data AnalysisYingjian Qi, Xinyan Yu, Guoliang Shi, Ying Li

Faculty of Science and Technology

Communication University of China

ICIS, USAIEEE 2015

• Nowadays the application of visualization technology is very wide while big data technology is rapidly developing.

• According to the micro-blog users forwarding hotspots public opinion, we introduce the geographic information data, which is able to be more in-depth data analysis and demonstrate the nature of the law of the things by visualization.

Introduction Content Conclusion

Visualization in Media Big Data Analysis

• Although traditional text analysis techniques achieve to dig out important information from large data, information is usually unable to meet the people’s requirements or to use the browser and screen them for a reasonable way to analyze, understand and apply.

• Faced with this challenge, text visualization technology emerges.

• Text visualization combines text analytics, data mining, data visualization, computer graphics, human-computer interaction, theory, and methods of cognitive science and other disciplines.



I. Text VisualizationA. Meaning of text visualization

• Text visualization system consists of three parts:• Producing visualization needs the process of text

analysis. This refers to the extraction of a substantial collection of descriptors (keywords, freedom words, heading, etc).

• Visual presentation. Text visual coding relates to size, shape, orientation, texture, etc.

• Interaction between user and information map. This mainly involves the associated update, highlighting, animated transitions, zooming, and so on.



I. Text VisualizationB. Content of Text Visualization

• The use of text visualization technology can produce rich charts and images, which can help people to summarize the results of the full text, analysis of the resulting data, and show it in a more easily understanding and accepting way.

• It plays a huge effect on intelligence research, decision support, and other related fields.

• Because of the development of text visualization, the emergence of micro-blog subverts the process of people browsing traditional text information and changes the traditional mode of transmission information.



I. Text VisualizationC. Text Visualization Application Status

• Micro-blog is a social networking platform to share real-time information through relationship social networking, which based on user information sharing, communication and achievement.

• Micro-blog has characteristics of convenience, originality, and grassroots.



II. Micro-Blog VisualizationA. Definition and Characteristics of Micro-Blog

• Currently, popular micro-blog visual analysis tools are ‘Sina micro-blog index’, ‘zhiwei’, ‘a find microanalysis’, ‘PKUVIS’, etc.

• Especially in the case of micro-blog trend of public opinion, the application of hot words index is very extensive. Only open the search interface, you will see the hot recent public opinion concerned by people.

• Each micro-blog forwarding path is clearly visible and we can distinguish forwarding hierarchy, so the most important is we can quickly find hot micro-blog articles of opinion leaders.



II. Micro-Blog VisualizationB. Analysis Tools of Micro-Blog Visualization

• Micro-blog becomes major positions for micro-blog marketing, government affairs and public opinion fermentation. Government should not be underestimated its power of spread and influence for individuals, businesses.

• Micro-blog visualization analyzes the dissemination of information, traces the source of hot events and identifies the authenticity of the relevant decision-making departments. Those are helpful to serve the public.

• The spread of corruption micro-blog, micro-blog crackdown, micro-blog marketing caused by micro-blog is changing our lives. Our life will be more colorful as long as we use micro-blog reasonably to spread information



II. Micro-Blog VisualizationC. Influence and Significance of Micro-Blog Visualization

• Geospatial information visualization is based on computer science, cartography, cognitive science, information science and GIS, so that it can visually interpret and transmit performance geospatial information and reveal its rule through computer technology and multimedia technology

• Da Liu etc discussed the exploring the ground based on three-dimensional volume rendering technology, which the prospects are very extensive



III. Geographical Information VisualizationA. Content of Geographical Information Visualization

• The geographic information visualization emerged gradually and has the following characteristics:• Intuitive and vivid. Geographic information visualization reveals information to the

reader though vivid visual image of graphics, images, video, sound, etc.

• Interactive exploratory. Interactive mode is helpful to visual thinking. Geographic workers can easily learn to use interactive way to compare, synthesize, and analyze more sources of information.

• Information carrier diversity. With the development of multimedia technology, expressing geography information is no longer limited to tables, graphs and documents, but extends to images, animation, 3D simulation, virtual reality, etc.



III. Geographical Information VisualizationB. Features of Geographic Information Visualization

• Visualization process mainly includes forming graph and querying spatial information:• Forming a graphic image. Geographic information includes a 2D image and 3D map

data as well as data analysis and evaluation of visual expression of plots, histograms, etc. GIS set up the many types of geographic phenomena graphics, images in the Windows environment.

• Spatial information query. Quick query spatial information is an important application of GIS visualization based on certain requirements. Spatial query GIS includes: spatial relationship queries, property characteristics queries, queries based on spatial relations and attribute characteristics.



III. Geographical Information VisualizationC. Process of Geographic Information Visualization

• It is not easy for people to deal with the mass of information nowadays.

• The text visualization technology is emerged that is helpful for people to extract information from the huge amount of information they are interested in.

• Micro-blog subverts the traditional mode of transmission of information, which is beneficial study the geographical information of communicators.



Visualization Time-Varying Topics via Images and Texts for Inter-Media AnalysisMasahiko Itoh, Masaru Kitsuregawa

Institute of Industrial Science

University of Tokyo

17th International Conference on Information VisualizationIEEE 2013

• Various types of content such as text, images, and videos have spread throughout multiple media, such as TV and the Web, that have complementary information and influence one another.

• It is important to compare how these media react to real world events to understand recent societal behaviors and how each medium reacts to other media.

• The proposed systems enable users to visually monitor changes in thought, activities, and interests of people, and differences between media through interactively exploring flows of texts and images extracted from the media.

Visualization Time-Varying Topics via Images and Texts for Inter-Media Analysis


Method Case Study Conclusion

• Hevre et al provides methods of visualizing changes in the values of multiple attributes on a timeline.

• Wei et al simultaneously combines ThemeRivew with tag-clouds to visualize changes in keywords consisting of topics.

• Dou et al provides flow-like metaphors and arranges these flows in parallel to represent topical themes over time.

• Imoto et al placed a set of polylines in 3D space to provide two different viewpoints that enabled users to explore both overviews of data from a top view and details of specific parts of data from a frontal view.

• Gomi et al visualized images categorized by time, location, and people in life log data to visualize flows of images.




• Flake’s pivot provided visualization of cover photographs of magazines from a particular facet

• Image depot visualized flows of images from captured data packets at every IP address to check inappropriate use of the Internet.

• Crandall et al proposed a method of predicting locations from the visual, textual, and temporal features of photographs that people took of the locations.

• Lie et al introduced a tag ranking method using visual and textual features to extract tags related to selected images




• The proposed system is implemented as a composite component of the IntelligentBox system (Okada et al), which is a component- based visual software development system for interactive 3D graphics applications.

• Our visualization system consists of two main parts such as the Image Flow View and Event view:

• Image Flow View• Our system visualizes changes in images in topics in 3D visualization space.




• It uses the x-axis for the timeline. We adapt a histogram of images by stacking images on the timeline to represent the flow of various images at each timing.

• It uses the y-axis to stack images on the topic with a specified time window. It enables us to find the birth timing, bursting points, changes in popular content, and the lifetime of trends for each topic.

• Topics represented by the histograms of images are arranged along the z-axis in the 3D space. Users can manually or automatically define the order of topics on the z-axis by using their rankings if they have them.




• Event View• When users select to the topic and timing, the Event View retrieves events related

to the topic and automatically moves to the point of timing on the timeline to display events belonging to the time window.

• We modify our event visualization component in the proposed system using text analysis mechanism (Itoh, Yoshinaga, Toyoda, Kitsuregawa). It consists of two components such as TimeSlice (Itoh, Toyoda, Kitsuregawa) and Timeflux (Itoh).




• Figure 3 has an example of an event tree for a selected keyword. We place a topic keyword at the center of the tree in the TimeSlice and arrange verbs, on which the keyword depends, around the keyword.

• A TimeFlux is a line of bubbles, in this case 3D polygon fonts, that visualizes changes in the amount of information such as the number of events within a given period of time (Figure 1)




• Case Study 1: Inter-Media Event Exploration on TV and Blogs• Extracting images from TV archive. News video images related to specified

keywords are extracted from a broadcast news video archive created by National Institute of Informatics in Japan.

• Extracting events from Blog archive. We first extract sentences in which the names of key people appeared from the blog archive for this purpose. We then extract phrase dependency structures, in which each dependency relation includes the key people, from the extracted sentences by using dependency analysis, and then construct the event database.

• Example of visualization. Figure 1 and 2 indicate the number of mentions on TV and blogs every day corresponding to the four key people. Figure 3 visualizes detailed events that represent the activities of Prime minister Kan and people’s thoughts and/or interests in his actions. We can also see that they appear in different image flows from Figure 4.




• Case Study 2: Visual Trends in Social Media• Extracting visual trends from Blog archive. Our system retrieves relevant articles

from our blog archive for a given query, and then extracts images and surrounding texts included in the articles. These images are clustered based on their visual, textual, and chronological similarities, and then visualized as an image flow.

• Example of visualization. Figure 5 visualizes clustered images for a given query related to ‘Prime Minister Hatoyama’, where the top 20 clusters are arranged from front to back according to their rankings. Figure 6 visualizes changes in trends of new products related to ‘kitkat’ confectionery in Japan.




• We proposed a visualization system to explore trends and events in various types of media such as TV and/or blogs through observing the flows of images and changes in event tree structures.

• Our approach could also be applied to images from other types of media such as Twitter and Flickr.

• Our future work includes expanding our current work that allows users to easily recognize the characteristic of events from image flows using statistical values such as the mean and variance of histograms to extract important events, and cross correlation histograms from different types of media to extract lead lags from them.




“Moon Phrases”: A Social Media Facilitated Tool for Emotional Reflection and WellnessMunmun De Choudhury, Michael Gamon, Aaron Hoff, Asta Roseway

Microsoft Research

ICSTIEEE 2013

• Social media platforms, including Twitter and Facebook provide a window onto the thoughts and feelings of individuals around small and big happenings in their lives.

• We hypothesize that identifying the changes in language, emotion, and social activity on social media would enable individuals to reflect in their own behavior over time and in a fine-grained manner, which are otherwise known to be difficult to keep track of

• We believe Moon Phrases thus bears the potential to act as a self-narrative or ‘behavioral fingerprint’, and thereby serve as an unobtrusive mechanism to facilitate emotional wellness in individuals.

“Moon Phrases”: A Social Media Facilitated Tool for Emotional Reflection and Wellness


Design Technical Conclusion

• Kotikalapudi et al analyzed patterns of web activity of college students that could signal emotional concerns

• Park et al found initial evidence that people do post about their affective concerns and even their treatment on Twitter.

• Choudhury et al examined linguistic and emotional correlates for postnatal course of new mothers as manifested in Twitter.

• Munson et al presented an application for Facebook that promotes health interventions.




• A core challenge of the design process was identifying what are the best social media cues to be visualized, based on an end user’s activity that can promote emotional wellness.

• Hence, a key aspect of Moon Phrases was the ability of users to observe how they use language, and how the usage of various style categories reflected their behavioral characteristics.

• Emotions are founded on interrelated patterns of cognitive processes, physiological arousal, and behavioral reactions.

• Weaving these observations together, or design process indicated Moon Phrases to be a mechanism to show trends of people’s social activity (i.e., volume of posting on Twitter), and affect over time; as well as usage of linguistic styles that might relate to people’s social and psychological environment. We chose one day as the granularity to show these trends.







• As Figure 1, the postings are organized based their timestamp of postings – most recent months are represented as rows at the top, and scrolling down one could browse the affect over previous timeframes.




• On clicking on any moon on a certain day, a user could view the particular date and day of the week, as well as each posting made on that day, including the degree of positivity expressed in each, through a smaller-sized moon (Fig 2)

• Fuller moons (the light-filled area of the moon in Figure 1) indicate greater positive affect.

• The moons that are white/lighter shade in color in the illuminated portion represented positive affect corresponding to 1-3 posts, whereas the orange/darker shade ones corresponded to more than three posts - thus an end user gets a sense of the general volume of posting that leads to certain measurement of affect.

• Each of the ‘daily’ moons is also interactive – on clicking on any moon on a certain day, a user could view the particular data and day of the week, as well as each posting made on that day, including the degree of positivity expressed in each, through a smaller-sized moon (Figure 2)




• Studies in Choudhury et al indicated that these hashtags often acted as a supervisory summary signal indicating a person’s affective state in the context of the post.

• These hashtags were then mapped into positive and negative affect and used as a training signal to identify affect from Twitter posts, based on a text classifier – a maximum entropy classifier trained on unigrams and bigrams of post content. This classifier has been validated on Twitter datasets, with mean accuracy of more than 85% for the two classes of affect.




• In this paper, we have not validated the Moon Phrases prototype in terms of its ability to act as an intervention mechanism for emotional wellness yet.

• Through our design was closely based on feedback from fluent social media users, in the future we intend to evaluate how Moon Phrases can positively impact their behavior, beyond revealing mere ‘signals’ of their affect and behavior.

• We are considering a longitudinal study design in which a set of users may be asked to use the web tool over the course of three to four weeks; with self-reported behavioral and emotional reflection surveys undertaken at the end of every week.




CosMovis: Semantic Network Visualization by Using Sentiment Words of Movie Review DataHyoji Ha, Wonjoo Hwang, Sungyun Bae, Hanmin Choi, Hyunwoo Han, Gi-nam Kim, Kyungwon Lee

Department of Digital Media

Ajou University

19th International Conference on Information VisualizationIEEE 2015

• Social Network Analysis performs a significant role in understanding and finding solutions of society-functional problems by examining the original structure and relationships of network.

• This paper proposed to discover the correlations between keywords through ‘Multi-dimensional Scaling: MDS’ and reflect the analysis result in a two-dimensional distribution map, to distribute nodes in semantic positions when designing network visualization based in similarities.

• We also applied a constellation map formed upon nodes and edges of a network clustering structure to label the characteristics of each cluster.

CosMovis: Semantic Network Visualization by Using Sentiment Words of Movie Review Data


Method Conclusion

• Sentiment Words• Kim et al covered the sentiment words shown in online postings.

• JoungYeon et al illustrated the adjectives to describe the texture of Haptic, and indicated the relations between adjectives on MDS.

• Network Visualization and Layouts• Dunne et al introduce a technique called motif simplification, in which common

patters of nodes and links are replaced with compact and meaningful glyphs, leading users to easily analyze network visualization.

• Uboldi et al presented a tool called ‘Knot’, aiming to analyze the multi-dimensional and heterogeneous data, while focusing on interface design and information visualization on multidisciplinary research context.

• Henry et al suggested the methods to solve the clustering ambiguity and increase readability in network visualization.



Method Conclusion

• Data Processing• Sentiment Words Collection

We selected 100 sentiment word based on Hahn and Kang’s research. We investigated to what degree of the emotion represented in each sentiment word can be drawn from watching the movies. The questionnaire used a 7-point Likert Scale from ‘strongly irrelevant’ to ‘strongly relevant’. After eliminating 32 sentiment words relatively under the average, 68 sentiment words were finally selected

• Sentiment Words Refinement

To select the final sentiment words from among 68 sentiment words, we collected and compared the sentiment word data in existing movie reviews, eliminating the words rarely used.

• CrawlingMovie review data were collected from NAVER, a web portal site with largest number of users in Korea. We designed a web crawler to automate the sentiment word collection from movie reviews.• Establishing sentiment word dictionaryWe divided the text data into morphemes collected through the crawling process. Extracting emotion morphemes and classifying then by category was conducted with the consultation of Korean linguists.



Method Conclusion



Method Conclusion

• Applying TF-IDF

We eliminated less influential sentiment word clusters after matching them with actual movie review data, in order to produce more accurate results. We eliminated the sentiment words of which the TF-IDF score was less than 10%, and eventually selected 36 sentiment words.

• Movie Data Collection

Movie samples used in network visualization were also collected from NAVER movie service in accordance with movie review data. As a result, 678 movie samples were selected and utilized as network sample data.

• Visualization Proposal• Heat Map Visualization

First, we measured space among the selected 36 sentiment words and analyzed its correlations. We conducted a survey on semantic distance among 20 college students. The sentiment words were on both axis (36x36) and the distance between words was scored by giving plus/minus 3 points, considering their emotional distance.



Method Conclusion

We measured the frequency of the sentiment words on each movie by contrasting sentiment words in the movie review data. Also, we measured numerical values by calculating TF-IDF score to lower the weight of particular sentiment words. Therefore, TF-IDF scores on each sentiment word could be interpreted as a numerical value reflected on the Heat Map Visualization Graph for target movies.



Method Conclusion

As Figure 4 B, it can be interpreted that there were various spectators with different emotions about this movie, includes disappointments. One indicated that only one characteristic showed high frequency among several sentiment words including ‘happy’, ‘surprise’, ‘boring’, ‘sad’, ‘anger’, ‘disgust’, and ’fear’ (Figure 4 A). Furthermore, using the Heat Map made it possible to easily compare movie nodes which have contrasting or similar sentiment words.



Method Conclusion

• Sentiment-Movie Network

We aim to explain the basic structure of suggested graphs and examples and that the location of nodes can be altered depending on the main sentiment word from the movie review. We connected Sentiment Words on 2D Scaling Map with Movie Network, we called Sentiment-Movie Network.



Method Conclusion

The first layer is called The Semantic Layer and it consist of Semantic Points based on the 36 sentiment words. The Semantic Point of the sentiment word is located at an initially set value and it stays immovable.

The second layer is called the Network Layer, which includes the nodes that comprise the movie network.

Each movie node forms the edge of other movie nodes based on similarities and also forms imaginary edges with the sentiment word based on sentiment word that the pertinent node connotes.



Method Conclusion

• Constellation Visualization

This chapter facilitates a cognitive understanding of the process to design constellation image visualization, based upon specific nodes and edges with significant sentiment word frequency to clarify the semantic parts of each clustering.

We created an asterism graphic of each cluster network, considering the significant sentiment words, information on movies, and synopses in each cluster.

Table 2 shows the main emotions and movie examples that each cluster has, and the motivates for choosing each asterism name.



Method Conclusion



Method Conclusion

• In order to efficient analyze network visualization, this research proposed Heat Map Visualization to understand the characteristics of each node, a method to describe the network nodes based upon a 2D sentiment word map and asterism graphic for the semantic interpretation of clustering.

• However We did not consider the relation between color tones and emotions when designing in satisfying the users’ possible needs to connect the node’s color with emotions.

• This research is expected to be adopted in another network system since our method is applicable regardless of the number of review data, and even to other media contents such as web-based cartoons, music, and books, using assorted constellation images related to target field.



Method Conclusion

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