TweetVista: An AI-Powered InteractiveTool for Exploring Conversations onTwitter

Prashanth VijayaraghavanMIT Media LabCambridge, MA 02139, USApralav@mit.edu

Soroush VosoughiMIT Media LabCambridge, MA 02139, USAsoroush@mit.edu

Ann YuanMIT Media LabCambridge, MA 02139, USAannyuan@gmail.com

Deb RoyMIT Media LabCambridge, MA 02139, USAdkroy@mit.edu

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AbstractWe present TweetVista, an interactive web-based tool formapping the conversation landscapes on Twitter. Tweet-Vista is an intelligent and interactive desktop web appli-cation for exploring the conversation landscapes on Twit-ter. Given a dataset of tweets, the tool uses advanced NLPtechniques using deep neural networks and a scalable clus-tering algorithm to map out coherent conversation clusters.The interactive visualization engine then enables the usersto explore these clusters. We ran three case studies us-ing datasets about the 2016 US presidential election andthe summer 2016 Orlando shooting. Despite the enormoussize of these datasets, using TweetVista users were able toquickly and clearly make sense of the various conversationtopics around these datasets.

Author KeywordsTwitter; Tweet2Vec; Conversation Clusters; Semantic Clus-ters; Interactive Tool

ACM Classification KeywordsH.5.m [Information Interfaces and Presentation (e.g. HCI)]:Miscellaneous; H.3.3 [Information Search and Retrieval]:Clustering

IntroductionTwitter should be an ideal place to get a fresh read on howdifferent issues are playing with the public, one that’s poten-tially more reflective of democracy in this new media agethan traditional polls. Pollsters typically ask people a fix setof questions, while in social media people use their ownvoices to speak about whatever is on their minds. Millionsare discussing politics and other issues every day on Face-book, Twitter, Instagram and other platforms, right alongsidethe candidates and the journalists covering them.

However, the sheer scale of the data on Twitter presentsboth opportunities and challenges. On the plus side, thisallows us to measure in aggregate how the various issuesare rising and falling in prominence over time. But unliketraditional news coverage, which is based on journalisticobservation, interviews and storytelling, data is about num-bers. It lacks the human voices and faces that make forcompelling stories. For years, media outlets have beenworking around this problem by paying attention to themost popular conversation on social media, often identifiedthrough hashtags, and anecdotally pulling out citizen com-ments on those topics. The problem with trending topics isthat can overlook non-viral issues that many people careabout. And anecdotally selected tweets are not necessarilyreflective of the larger conversation.

Using recent advances in deep neural networks for naturallanguage processing, we developed a way to automaticallyidentify various clusters of any conversation on Twitter. Wealso built a web-based interactive tool, called TweetVista,on top of our system that allows users to explore the con-versation landscapes identified by our system. The follow-ing sections contain detailed descriptions of various com-ponents of the tool, implementation details of the tool andcase studies.

TweetVistaTweetVista is composed of three parts: (a) a sophisticatedmechanism for extract rich semantic features from the tweettext, (b) a scalable methodology to agglomerate semanti-cally similar tweets into a cluster, (c) an interactive endpointto visualize the tweet clusters. Below we explain each ofthese three sections in detail.

Tweet2VecDue to the noisy nature of tweets, commonly used meth-ods to extract semantic features such as TF-IDF [6] anddistributed word vectors [4], operating at word-level, donot perform well. Therefore, we utilized Tweet2Vec [7], acharacter-level CNN-LSTM encoder-decoder approach,to learn general purpose vector representation of tweets.These vectors capture abstract semantic structures thatcan be applied to several generic tasks. Tweet2Vec [7] is arecent method for generating general-purpose vector rep-resentation of tweets. Tweet2Vec removes the need for ex-pansive feature engineering and can be used to train anystandard off-the-shelf classifier (e.g., logistic regression,svm, etc). It uses a CNN-LSTM encoder-decoder modelthat operates at the character level and can deal with thenoise and idiosyncrasies in tweets. Character-level modelsare great for noisy and unstructured text since they are ro-bust to errors and misspellings in the text. The model learnsabstract textual concepts from the character level input oftweets.

We trained our model on 5 million randomly selected English-language tweets populated using data augmentation tech-niques, which are useful for controlling generalization er-ror for deep learning models. Data augmentation involvedreplacing some of the words with their synonyms as men-tioned in [8, 7].

ClusteringNext, we cluster the tweets based on the tweet embeddingsgenerated by Tweet2Vec (with a vector size of 256) to ag-gregate semantically similar tweets into a topic bucket. Thisrequires a scalable clustering technique that can take alarge number of tweets as input and cluster them in a non-parameterized setting. We used a scalable, non-parameterized

Case StudiesWe tested TweetVista onthree different datasets: 1)Trump’s Immigration Speech,2015, 2) Orlando Shooting& aftermath, 2016, 3) Dis-cussion of US Economy onTwitter, Summer 2016.

These datasets were col-lected using a state-of-the-artsupervised Twitter topic clas-sifier [5]. (The details of thetopic classifier are out of thescope of this paper, pleaseread the cited paper for moredetails.)

Figure 2 shows a 2D viewof the conversation clustersgenerated by TweetVista, forthe "US economy" dataset.In the interest of brevity wedo not show the clustersfor all the datasets but allthree datasets can be fullyexplored in 2D and 3D usingour tool on TweetVista.com. Ascan be seen, an interestingnarrative emerges from thesetweet clusters.

hierarchical density-based clustering algorithm called Hier-archical DBSCAN (HDBSCAN), introduced by Campello,et.al[1]. HDBSCAN, is a clustering algorithm that can beseen as an improvement over existing density-based clus-tering algorithms.

Visualization EngineFinally, the visualization engine renders clusters of seman-tically related tweets as a particle cloud. Users can exploretweets by panning, rotating, or zooming the cloud. Userscan filter the tweets shown by properties of their content orauthors. The interface also includes details regarding eachsemantic cluster. Users can choose between several differ-ent 2D and 3D datasets to visualize using the tool.

The first step for visualization is the reduction of the high di-mensional tweet embeddings to two or three dimensions.We used t-SNE for this task [3]. t-SNE is a variation ofStochastic Neighbor Embedding [2] that is easier to opti-mize, and produces significantly better visualizations byminimizing the tendency to crowd points together in thecenter of the map.

Interface OverviewTweetVista is a desktop web application best viewed withGoogle Chrome. A short video, fully describing the inter-face can be found at https://youtu.be/BBtZ6P4FLds. Figure 1shows a screenshot of TweetVista’s interface. Upon loadingTweetVista, users see a visualization of the default dataset.Users can visualize other datasets by making a selection

Figure 1: Screenshot of the entire interface of TweetVista.

within a pop-up menu that appears when they click on thetitle of the interface. Tweets are represented by particleswhose position in 3D is determined by the t-SNE algorithmdescribed earlier. Users can zoom into the cloud of tweetsby using their mouse wheel or trackpad, they can rotateit by dragging along the interface, and they can also panthe cloud’s position by pressing their arrow keys. Tweetparticles are colored according to the conversation clus-ter they belong to. Users can see the text, author, and dateof each tweet by hovering over it. Users can filter the tweetsshown by content properties such as the civility of the tweet(e.g., whether the tweet contains profanity), or propertiesof the author such as whether the account is verified, theauthor’s number of followers, statuses, followees, etc. Userscan also filter the date range from which data is drawn bymanipulating the timeline at the bottom of the interface.They can select a particular pre-defined date interval, orpress the “play” icon, which allows them to see the clustersforming over time. The interface also includes a side-panelthat provides details about each semantic cluster of tweets

Figure 2: Conversation clusters around the topic of the USeconomy in the context of 2016 presidential election.

(such as the most frequently occurring words in the cluster)in the currently visualized dataset.

ConclusionIn this paper, we presented TweetVista, an interactive toolfor mapping the conversation landscapes on Twitter, to bet-ter understand what the Twitter public is saying about vari-ous issues. TweetVista has two main components: 1) Iden-tification of conversation clusters for a given dataset, 2) Aninteractive visualization enabling users to explore the land-scape of tweet conversations for a given topic. We testedTweetVista on three datasets from 2015 and 2016.

TweetVista has at its core, a powerful semantic analysisengine that utilizes recent advances in natural languageprocessing using deep neural networks. In contrast to sim-ilar tools, our tool was specifically designed to deal with theshort, noisy and idiosyncratic nature of tweets. TweetVistaenables users to make sense of large volumes of tweetsabout any given topic.

REFERENCES1. Ricardo JGB Campello, Davoud Moulavi, and Jörg

Sander. 2013. Density-based clustering based onhierarchical density estimates. In Pacific-AsiaConference on Knowledge Discovery and Data Mining.Springer, 160–172.

2. Geoffrey E Hinton and Sam T Roweis. 2002. Stochasticneighbor embedding. In Advances in neural informationprocessing systems. 833–840.

3. Laurens van der Maaten and Geoffrey Hinton. 2008.Visualizing data using t-SNE. Journal of MachineLearning Research 9, Nov (2008), 2579–2605.

4. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg SCorrado, and Jeff Dean. 2013. Distributedrepresentations of words and phrases and theircompositionality. In Advances in neural informationprocessing systems. 3111–3119.

5. Prashanth Vijayaraghavan, Soroush Vosoughi, andDeb Roy. 2016. Automatic Detection andCategorization of Election-Related Tweets. InProceedings of the Tenth ICWSM.

6. Soroush Vosoughi and Deb Roy. 2016. ASemi-Automatic Method for Efficient Detection ofStories on Social Media. In Proceedings of the TenthICWSM.

7. Soroush Vosoughi, Prashanth Vijayaraghavan, andDeb Roy. 2016. Tweet2Vec: Learning TweetEmbeddings using Character-level CNN-LSTMEncoder-Decoder. In Proceedings of the 39th SIGIR.

8. Xiang Zhang and Yann LeCun. 2015. TextUnderstanding from Scratch. arXiv preprintarXiv:1502.01710 (2015).