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Mining Twitter for Real-Time Trend and Information Discovery
 

Mining Twitter for Real-Time Trend and Information Discovery

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    Mining Twitter for Real-Time Trend and Information Discovery Mining Twitter for Real-Time Trend and Information Discovery Presentation Transcript

    • Mining Twitter for real-time trend and information discovery Yahoo! Research Barcelona Arkaitz Zubiaga NLP & IR Group @ UNED December 19th, 2011
    • Motivation Index 1 Motivation 2 Our Work (I): Classification of Trending Topics 3 Our Work (II): Real-Time Summarization of Events 4 Outlook Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 2 / 43
    • Motivation Twitter Twitter is a microblogging service with over 200 million users. Users share short messages of up to 140 characters (tweets). Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 3 / 43
    • Motivation Twitter: following users Different from Facebook, following is not reciprocal. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 4 / 43
    • Motivation Twitter: retweeting Retweet: users can help spread tweets by others. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 5 / 43
    • Motivation Twitter Retweeting enables fast spread of messages. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 6 / 43
    • Motivation Increase of activity on Twitter As of October 2011, Twitter received 250 million tweets per day. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 7 / 43
    • Motivation Variety of Twitter accounts Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 8 / 43
    • Motivation Usefulness of Twitter Twitter provides... 1 ...large amounts of data in real-time, 2 from a wide variety of sources, 3 with the ability to spread rapidly. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 9 / 43
    • Motivation Twitter’s popularity Twitter has gained widespread popularity as a tool for... Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 10 / 43
    • Motivation Using Twitter for... following events (1) Live-tweeting about and following events. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 11 / 43
    • Motivation Using Twitter for... helping others (2) Helping others, as in natural disasters. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 12 / 43
    • Motivation Using Twitter for... finding out about news and (3) Finding out about breaking news. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 13 / 43
    • Motivation Twitter on the media Lots of researchers are analyzing tweets. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 14 / 43
    • Motivation Trends on Twitter The news about the Japan earthquake broke on Twitter. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 15 / 43
    • Motivation Video: Japan earthquake on Twitter Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 16 / 43
    • Motivation Research on Twitter Most of the research on Twitter focus on the analysis of streams after they happened. Very little research deals with the real-time analysis of streams. Our goal: How can we mine Twitter streams to acquire real-time knowledge about events and trends? Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 17 / 43
    • Our Work (I): Classification of Trending Topics Index 1 Motivation 2 Our Work (I): Classification of Trending Topics 3 Our Work (II): Real-Time Summarization of Events 4 Outlook Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 18 / 43
    • Our Work (I): Classification of Trending Topics Trending Topics on Twitter Trending topics reflect the top conversations being discussed on Twitter more than usually. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 19 / 43
    • Our Work (I): Classification of Trending Topics What produces trending topics? What kinds of events leverage those trending topics? Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 20 / 43
    • Our Work (I): Classification of Trending Topics Typology of Trending Topics News: Japan earthquake. Current events: a soccer game. Memes: funny and viral ideas. Commemoratives: World AIDS Day. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 21 / 43
    • Our Work (I): Classification of Trending Topics Goal Find out the type of a trending topic as soon as it emerges. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 22 / 43
    • Our Work (I): Classification of Trending Topics Dataset 1,036 unique trending topics, with up to 1,500 associated tweets as soon as they trended. Manual classification of trending topics: 616 current events. 251 memes. 142 news. 27 commemoratives. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 23 / 43
    • Our Work (I): Classification of Trending Topics Experiment Settings Support Vector Machines (one-against-all) 500 trends for the training set. 10 runs. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 24 / 43
    • Our Work (I): Classification of Trending Topics Representation of Trending Topics 2 different representation approaches: Twitter features: 15 straightforward language-independent features that rely on the social spread of trends. Bag-of-words: Text of tweets (TF). Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 25 / 43
    • Our Work (I): Classification of Trending Topics Results Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 26 / 43
    • Our Work (I): Classification of Trending Topics Results Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 27 / 43
    • Our Work (I): Classification of Trending Topics Main findings Trending topics can accurately (78.4%) be categorized using social features: Outperforming use of textual content. Without making use of external data. In real-time as the trending topic emerges. Arkaitz Zubiaga, Damiano Spina, V´ ıctor Fresno, and Raquel Mart´ ınez. 2011. Classifying trending topics: a typology of conversation triggers on Twitter. CIKM 2011. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 28 / 43
    • Our Work (II): Real-Time Summarization of Events Index 1 Motivation 2 Our Work (I): Classification of Trending Topics 3 Our Work (II): Real-Time Summarization of Events 4 Outlook Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 29 / 43
    • Our Work (II): Real-Time Summarization of Events Events on Twitter When users live-tweet about events: They produce vast amounts of tweets about events. Users want to follow what others say. Users cannot follow the overwhelming amounts of tweets. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 30 / 43
    • Our Work (II): Real-Time Summarization of Events Stream summarization Can we summarize streams of tweets in such a way that: Users receive a reduced stream that they can follow? Users do not miss any key sub-event occurred during the event? Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 31 / 43
    • Our Work (II): Real-Time Summarization of Events Study of soccer games Copa America 2011 (July 1-26, 2011): 26 soccer games. 11k-70k tweets per game. Tweets are written in 30 languages. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 32 / 43
    • Our Work (II): Real-Time Summarization of Events Gold Standard Live reports gathered from Yahoo! Sports. Yahoo! journalists provide annotations for: Goals. Penalties. Red Cards. Disallowed Goals. Game Starts, Ends, Stops & Resumptions. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 33 / 43
    • Our Work (II): Real-Time Summarization of Events Histogram of a Soccer Game 2500 tweet rate 2000 1500 1000 1310864000 1310862000 1310860000 1310858000 1310856000 1310854000 500 time elapsed Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 34 / 43
    • Our Work (II): Real-Time Summarization of Events Summarization of soccer games 2-step summarization: 1 Sub-event detection. 2 Tweet selection. Sub-event Detection Tweet Selection tweet tweet tweet summary tweets stream real-time Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 35 / 43
    • Our Work (II): Real-Time Summarization of Events 1st Step: Sub-event Detection Increase [Zhao et al., 2011]: a sub-event occurred when a sudden increase is given in the tweeting rate (1.7 as much as the previous rate). Outliers: learns from audience. High tweeting rates as compared to rates seen so far will be considered sub-events (90% percentile). Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 36 / 43
    • Our Work (II): Real-Time Summarization of Events 1st Step: Results Increase Outliers P 0.29 0.51 R 0.81 0.84 F1 0.41 0.63 # 45.4 25.6 Increase-based approach provides more sub-events, with many FPs (recall-based). Outlier-based approach (rather based on outstanding tweeting rates) improves in P and R. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 37 / 43
    • Our Work (II): Real-Time Summarization of Events 2nd Step: Tweet Selection Each term appearing in tweets in a given timeframe is given a weight according to: Frequency (TF). Language Models (KLD). These weightings enable to choose a representative tweet, as the tweet with higher value adding up weights of its terms. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 38 / 43
    • Our Work (II): Real-Time Summarization of Events 2nd Step: Results es en pt Goals (54) TF KLD 0.98 1.00 0.98 1.00 0.98 1.00 Penalties (2) TF KLD 1.00 1.00 0.50 0.50 1.00 1.00 Red cards (12) TF KLD 0.75 0.92 0.75 0.92 0.83 1.00 Disallowed goals (10) TF KLD 0.40 0.40 0.50 0.50 0.40 0.30 Game starts (26) TF KLD 0.73 0.84 0.74 0.79 0.79 0.83 Game ends (26) TF KLD 1.00 1.00 1.00 1.00 1.00 1.00 Game stops & resumptions (63) TF KLD 0.62 0.68 0.60 0.60 0.57 0.59 Overall TF KLD 0.79 0.84 0.74 0.77 0.78 0.82 Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 39 / 43
    • Our Work (II): Real-Time Summarization of Events Main findings Use of state-of-the-art text analysis methods generates accurate summaries: With precision and recall values above 80% (100% for key sub-events). In real-time as the game is being played. In 3 different languages (es, en, pt). Without need of external data. Damiano Spina, Arkaitz Zubiaga, Enrique Amig´, Julio Gonzalo. Towards o Real-Time Summarization of Events from Twitter Streams. To Appear. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 40 / 43
    • Outlook Index 1 Motivation 2 Our Work (I): Classification of Trending Topics 3 Our Work (II): Real-Time Summarization of Events 4 Outlook Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 41 / 43
    • Outlook Outlook Work 1: Further dig into each type of trending topic, in order to look for subtypes of trends. Work 2: Evaluate the performance of the summarizer on other kinds of scheduled events (award ceremonies, keynote talks,...) Evaluate novelty of information garnered from tweets. Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 42 / 43
    • Outlook Any Questions? Arkaitz Zubiaga (UNED) Real-time mining of Twitter December 19th, 2011 43 / 43