emnlp emnlp2013 emnlp2013-16 knowledge-graph by maker-knowledge-mining

16 emnlp-2013-A Unified Model for Topics, Events and Users on Twitter


Source: pdf

Author: Qiming Diao ; Jing Jiang

Abstract: With the rapid growth of social media, Twitter has become one of the most widely adopted platforms for people to post short and instant message. On the one hand, people tweets about their daily lives, and on the other hand, when major events happen, people also follow and tweet about them. Moreover, people’s posting behaviors on events are often closely tied to their personal interests. In this paper, we try to model topics, events and users on Twitter in a unified way. We propose a model which combines an LDA-like topic model and the Recurrent Chinese Restaurant Process to capture topics and events. We further propose a duration-based regularization component to find bursty events. We also propose to use event-topic affinity vectors to model the asso- . ciation between events and topics. Our experiments shows that our model can accurately identify meaningful events and the event-topic affinity vectors are effective for event recommendation and grouping events by topics.

Reference: text


Summary: the most important sentenses genereted by tfidf model

sentIndex sentText sentNum sentScore

1 On the one hand, people tweets about their daily lives, and on the other hand, when major events happen, people also follow and tweet about them. [sent-5, score-0.997]

2 In this paper, we try to model topics, events and users on Twitter in a unified way. [sent-7, score-0.578]

3 Our experiments shows that our model can accurately identify meaningful events and the event-topic affinity vectors are effective for event recommendation and grouping events by topics. [sent-12, score-1.696]

4 The concepts of topics and events are orthogonal in that many events fall under certain topics. [sent-31, score-0.998]

5 Furthermore, being social media, Twitter users play important roles in forming topics and events on Twitter. [sent-33, score-0.784]

6 Whether a user publishes a tweet related to an event also largely depends on whether her topic interests match the nature of the event. [sent-35, score-0.931]

7 Modeling the interplay between topics, events and users can deepen our understanding of Twitter content and potentially aid many predication and recommendation tasks. [sent-36, score-0.647]

8 In this paper, we aim to construct a unified model of topics, events and users on Twitter. [sent-37, score-0.578]

9 Although there has been a number of recent studies on event detection on Twitter, to the best of our knowledge, ours is the first that links the topic interests of users to their tweeting behaviors on events. [sent-38, score-0.82]

10 Specifically, we propose a probabilistic latent variable model that identifies both topics and events on Twitter. [sent-39, score-0.581]

11 To do so, we first separate tweets into topic tweets and event tweets. [sent-40, score-1.222]

12 The latter are about some major global event interesting to a large group of people, such as a tweet advertising a concert or commenting on an election result. [sent-44, score-0.7]

13 Although considering only topic tweets and event tweets is a much simplified view of the diverse range of tweets, we find it effective in finding meaningful topics and events. [sent-45, score-1.459]

14 It uses the latent topics to explain users’ preferences of events and subsequently infers the association between topics and events. [sent-57, score-0.745]

15 Comparison with our base model and with an existing model for event discovery on Twitter shows that the two modifications are both effective. [sent-60, score-0.552]

16 The duration-based regularization helps find more meaningful events; the event-topic affinity vectors improve an event recommendation task and helps produce a meaningful organization of events by topics. [sent-61, score-1.419]

17 2 Related Work Study of topics, events and users on Twitter is related to several branches of work. [sent-62, score-0.578]

18 In comparison, our work focuses on modeling topics, events and users as well as their relation. [sent-72, score-0.578]

19 Our work helps better understand these additional events on Twitter and their relations with users’ topic interests. [sent-75, score-0.561]

20 It has later been combined with LDA to model both topics and events in news streams and social media streams (Ahmed et al. [sent-82, score-0.623]

21 3 Our Model In this section, we present our model for topics, events and users on Twitter. [sent-90, score-0.578]

22 3 we discuss how we model the relation between topics and events using event-topic affinity vectors. [sent-118, score-0.827]

23 Each event is also a multinomial distribution over words, denoted as ψk where k is an event index. [sent-124, score-0.915]

24 As we have discussed, we separate tweets into two categories, topic tweets and event tweets. [sent-129, score-1.222]

25 For event tweets, the event is sampled according to RCRP. [sent-132, score-0.852]

26 It could be an existing event that has at least one related tweet in the previous epoch or the current epoch, or it could be a new event. [sent-139, score-0.751]

27 Let nk,t−1 denote the number of tweets related to event k at the end of epoch (t 1). [sent-140, score-0.891]

28 Let − n(ki,t) denote the number of tweets related to event k in epoch t before the i-th tweet comes. [sent-141, score-1.077]

29 Let Nt−1 denote the total number of event-related tweets in epoch (t − 1) and denote the number of eventereploatcehd ( ttw −ee 1ts) ainn epoch t before the i-th tweet. [sent-142, score-0.604]

30 Then RCRP assumes that the probability for the i-th tweet Nt(i) Nntk−,t1+−1N+t(ni)(k+i,t)α and the probability to start a new event is Nt−1+αNt(i)+α, where α is a to join event k is parameter. [sent-143, score-1.038]

31 We also show the plate notation in Figure 1, in which the Recurrent Chinese Restaurant Process is represented as an infinite dynamic mixture model (Ahmed and Xing, ) and θrtcrp means the distribution on an infinite number of events in epoch t. [sent-147, score-0.586]

32 Dt is the total number of tweets (both event-related and topic tweets), while Nt represents the number event-related tweets in epoch t. [sent-148, score-0.935]

33 If yt,i is 1, then we look at all the tweets that belong to event st,i. [sent-159, score-0.752]

34 So we assume that rt,i gets a value of 1 with probability exp(− nst,i,t′ ∑tT′=1,|t′−t|>1 λ|t − t′|nst,i,t′), where is the n∑umber of tweets in epoch t′ that be1872 • For each topic a = 1, . [sent-161, score-0.609]

35 , U) - drr eawac hθ uu ∼ Dirichlet(γ) , πu ∼ Beta(τ) • For each epoch itr iacnhdl tewt(eγe)t, ,iπ - drr eawac yt,i ∼ Bernoulli(πut,i ) - If yt,i = 0∼ * draw zt,i ∼ Multinomial(θut,i ) * For each j,d Mrauwl wt,i,j ∼ Multinomial(ϕzt,i ) - If yt,i = 1 * draw st,i from RCRP * If st,i is a new event . [sent-167, score-0.773]

36 We can see that when we factor in the generation of these pseud-observed variables r, we penalize long-term events and favor events whose tweets are concentrated along the timeline. [sent-174, score-1.192]

37 3 Event-Topic Affinity Vectors So far in our model topics and events are not related. [sent-177, score-0.581]

38 One way to do it is to assume that event tweets also have topical words sampled from the event’s topic distribution, something similar to the models by Ahmed et al. [sent-181, score-0.896]

39 The idea is that when a user posts a tweet about an event, we can treat the event as an item and this posting behavior as adoption of the item. [sent-185, score-0.736]

40 ηk0 is a bias term that represents the inner popularity of an event regardless of its affinity to any topic. [sent-191, score-0.834]

41 For event tweets, ct,i is generated by a Gaussian distribution with mean equal to ηs0t,i + ηst,i · z¯ut,i, where z¯u is an A-dimensional vector denoting the empirical topic distribution of user u’s tweets. [sent-194, score-0.724]

42 Let C¯u,a be the number of tweets by user u assigned to topic a, based on the values of the latent variables y and z. [sent-196, score-0.596]

43 In the gradient descent part, we update the event-topic affinity vectors ηk and the bias term ηk0 of each event k by keeping the assignment of the variables yt,i ,zt,i and st,i fixed. [sent-204, score-0.752]

44 ,nst−+(uaπ1,0n)>eiws0 teh,vn ek n,tu=m>b0 e,r n(uπ,1) of topic tweets by user u while is the number of event tweets by user u. [sent-213, score-1.41]

45 the total number of tweets is the number of tweets assigned to topic a for this user, resulting from integrating out the user’s topic nu(θ,(). [sent-217, score-0.94]

46 ) times word type v is assigned to event k, and is the total number of words assigned to event k. [sent-225, score-0.852]

47 Iu = {t′, i′|yt′,i′ = 1, ut′,i′ = u}, which is the set of event ttwee|yts published by user u, and u represents ut,i for short. [sent-227, score-0.52]

48 Finally, N is a local normalization factor for event tweets, which includes the RCRP, event-topic affinity and regularization on event duration. [sent-229, score-1.165]

49 Given the assignment, we use gradient descent to update the values of the bias term ηk0 and the eventtopic affinity vectors ηk for each current existing event k. [sent-231, score-0.77]

50 First, we can get the logarithm of the posterior distribution: ln P(y, z, s, r, c|w, u, all priors) = constant −k∑∞=1{2ι(ηk02+ ηk· ηk) +u∑U=1nu,k2ϵ[1 − (ηk0+ ηk· z¯ u)]2}, where nu,k is the number of event tweets about event k published by user u. [sent-232, score-1.272]

51 We therefore would like to evaluate the quality of the identified topics and events as well as the usefulness of the discovered topic distributions of users and eventtopic affinity vectors. [sent-250, score-1.228]

52 TimeUserLDA also mod- els topics and events by separating topic tweets from event tweets. [sent-258, score-1.477]

53 However, it groups event tweets into a fixed number of bursty topics and then uses a twostate machine in a postprocessing step to identify events from these bursty topics. [sent-259, score-1.485]

54 We do not compare with other event detection methods because our objective is not online event detection. [sent-262, score-0.885]

55 When a new event k is created, the inner popularity bias term ηk0 is set to 1, and the factors in event- topic affinity vectors ηk are all set to 0. [sent-271, score-1.026]

56 We then judge whether the detected event tweets are indeed related to the corresponding event. [sent-286, score-0.752]

57 For each method, we rank the detected events based on the number of tweets assigned to them and then pick the top-30 events for each method. [sent-306, score-1.189]

58 The judges are given 100 randomly selected tweets for each event (or all tweets if an event contains less than 100 tweets). [sent-308, score-1.537]

59 Finally we treat an event as meaningful if both judges have scored it 1. [sent-314, score-0.532]

60 We have the following findings from the results: (1) Our base model performs quite poorly for the top events while Base+Reg and Base+Reg+Aff perform much better. [sent-318, score-0.543]

61 A close examination shows that the base model clusters many general topic tweets as events, such as tweets about transportation and music and even foursquare tweets. [sent-320, score-0.966]

62 (2) TimeUserLDA performs well for the very top events (P@5 and P@ 10) but its performance drops for lower-ranked events (P@20 and P@30), similar to what was reported by Diao et al. [sent-321, score-0.834]

63 A close examination shows that this method is good at finding major events that do not have strong topic association and thus attract most people’s attention, e. [sent-323, score-0.598]

64 This is because this method mixes topics and events first and only detects events from bursty topics in a second stage of postprocessing. [sent-326, score-1.238]

65 (3) The difference between Base+Reg and Base+Reg+Aff is small, suggesting that the event- topic affinity vectors are not crucial for event detection. [sent-328, score-0.864]

66 Precision of Event Tweets Next, we evaluate the relevance of the detected event tweets to each event. [sent-329, score-0.752]

67 We pick 3 out of 5 common events shared by all methods within top-30 events Event TimeUserLDA Father’s Day debate caused by Manda Swaggie Indonesia tsunami Super Junior album release Base Base+Reg Base+Reg+Aff 0. [sent-331, score-0.964]

68 81 Table 3: Precision of the event tweets for the 4 common events. [sent-346, score-0.752]

69 The precision of the 100 tweets for each event and each method is shown in Table 3. [sent-350, score-0.752]

70 For example, for the event “Super Junior album release,” Base finds other music-related tweets surrounding the peak period of the event itself. [sent-353, score-1.241]

71 In summary, our evaluation on event quality shows that (1) Using the non-parametric RCRP model to identify events within the generative model itself is advantageous over TimeUserLDA, which identifies events by postprocessing. [sent-354, score-1.26]

72 3 Event-Topic Association Besides event identification, our model also finds the association between events and topics through the event-topic affinity vectors. [sent-357, score-1.253]

73 Event Recommendation Recall that to discover event-topic association, we treat an event as an item and a tweet about the event as indication of the user’s adoption of the item. [sent-360, score-1.094]

74 Following this analogy with item recommendation, we define an event recommendation task where the goal is to recommend an event to users who have not posted any tweet about the event but may potentially be interested in the event. [sent-361, score-1.694]

75 Intuitively, if a user’s topic 1876 distribution is similar to the event-topic affinity vector of the event, then the user is likely to be interested in the event. [sent-362, score-0.514]

76 We then use a ransom subset of 250 training users and their tweets in June to identify events in June as well as the event-topic affinity vectors of these events. [sent-364, score-1.198]

77 We pick 8 meaningful events that are ranked high by all methods for testing. [sent-365, score-0.519]

78 For each event, we try to find among the remaining 250 users those who may be interested in the event and compare the results with ground truth obtained by human judgment. [sent-366, score-0.587]

79 For each test user and each event, we manually inspect the user’s tweets around the peak days ofthe event tojudge whether she has com- mented on the event. [sent-368, score-0.846]

80 For the other methods, because we do not have· any parameter that directly encodes event-topic association, we cannot rank users based on how similar their topic distributions are to the event’s affinity to topics. [sent-371, score-0.551]

81 In addition, for each test event these methods identify a list of training users who have tweeted on it. [sent-374, score-0.641]

82 We also rank the 8 events in decreasing order of their inner popularity ηk0 learned by our complete model. [sent-421, score-0.579]

83 (1) Our complete method outperforms the other methods for 6 out of the 8 test events, suggesting that with the inferred event-topic affinity vectors we can do better event recommendation. [sent-424, score-0.72]

84 (2) The improvement brought by the eventtopic affinity vectors, as reflected in the difference in Average Precision between Base+Reg+Aff and Base (or Base+Reg) is more pronounced for events with average lower inner popularity. [sent-425, score-0.791]

85 The finding above suggests that the event-topic affinity vectors are especially useful for recommending events that attract only certain people’s attention, such as those related to sports, music, etc. [sent-427, score-0.748]

86 One may wonder for the events with low inner popularity why we could not achieve the same effect by Base or Base+Reg where we consider the topic similarity of test users with training users who have tweeted about the event. [sent-428, score-1.099]

87 Our close examination shows that for these events although Base and Base+Reg may identify relevant event tweets with decent precision, the users they identify who have tweeted about the event may not share similar topic interests. [sent-429, score-1.954]

88 As a result, when we average these users’ topic interests, we cannot obtain a clear skewed topic distribution that explains the event’s affinity to different topics. [sent-430, score-0.564]

89 In contrast, Base+Reg+Aff explicitly models the event-topic affinity vector and prefers to assign a tweet to an event if its author’s topic distribution is similar to the event’s affinity vector. [sent-431, score-1.278]

90 Through the training iterations, the users who have tweeted about an event as identified by Base+Reg+Affwill gradually converge to share similar topic distributions. [sent-432, score-0.785]

91 Grouping Events by Topics Finally, we show that the event-topic affinity vectors can also be used to group events by topics. [sent-433, score-0.711]

92 In Table 5 we show a few highly related events for a few popular topics in our Twitter data set. [sent-435, score-0.581]

93 Specifically given a topic a we rank the meaningful events that contain at least 70 tweets based on ηk,a. [sent-436, score-0.96]

94 The event “LionsXII 9-0 Sabah FA” is particularly interesting in that it is highly related to both the topic on Malay and the topic on soccer. [sent-438, score-0.714]

95 ) 5 Conclusion In this paper, we propose a unified model to study topics, events and users jointly. [sent-440, score-0.578]

96 The base of our method is a combination of an LDA-like model and the Recurrent Chinese Restaurant Process, which aims to model users’ longstanding personal topic interests and events over time simultaneously. [sent-441, score-0.798]

97 We further use a time durationbased regularization to capture the fast emergence and disappearance of events on Twitter, which is effective to produce more meaningful events. [sent-443, score-0.557]

98 Finally, we use an inner popularity bias parameter and event-topic affinity vectors to interpret an event’s inherent popularity and its affinity to different topics. [sent-444, score-0.786]

99 Our experiments quantitatively show that our proposed model can effectively identify meaningful 1878 events and accurately find relevant tweets for these events. [sent-445, score-0.816]

100 Furthermore, the event-topic association in- ferred by our model can help an event recommendation task and organize events by topics. [sent-446, score-0.912]


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