Twitter Topic Modeling for Breaking News Detection
Henning M. Wold
, Linn Vikre
, Jon Atle Gulla
and Xiaomeng Su
Department of Computer and Information Science, NTNU, Trondheim, Norway
Department of Computer Engineering, Balikesir University, Balikesir, Turkey
Department of Informatics and e-Learning, NTNU, Trondheim, Norway
Twitter, Topic Modeling, News Detection, Text Mining.
Social media platforms like Twitter have become increasingly popular for the dissemination and discussion
of current events. Twitter makes it possible for people to share stories that they find interesting with their
followers, and write updates on what is happening around them. In this paper we attempt to use topic models
of tweets in real time to identify breaking news. Two different methods, Latent Dirichlet Allocation (LDA)
and Hierarchical Dirichlet Process (HDP) are tested with each tweet in the training corpus as a document by
itself, as well as with all the tweets of a unique user regarded as one document. This second approach emulates
Author-Topic modeling (AT-modeling). The evaluation of methods relies on manual scoring of the accuracy
of the modeling by volunteered participants. The experiments indicate topic modeling on tweets in real-time is
not suitable for detecting breaking news by itself, but may be useful in analyzing and describing news tweets.
Social media networks facilitate communication be-
tween people across the world. Social networks not
only make it easier for people to communicate, they
also make it possible for the media to capture break-
ing news as they are emerging. Social media networks
have been used to provide information in real-time
about larger crisis situations such as earthquakes and
tsunamis (Mendoza et al., 2010).
Twitter is one such social network and a micro-
blogging service founded in 2006. As of 2014 the
company reports having 284 million active users per
. The service is focused on micro messages,
called tweets, which are restricted to a length of 140
characters. In addition to posting tweets about any-
thing, users can also follow other users.
In (Sakaki et al., 2010) it is examined how earth-
quakes could be detected using Twitter. In this re-
search Twitter users are treated as sensors and the
tweets as sensor data. By using this approach it was
possible to detect 96% of the earthquakes with inten-
sity of 3 or more occurring in the examined area. In
(Hu et al., 2012) it is showed how the news of Osama
Bin Laden’s death spread on Twitter before the mass
media could get the news confirmed.
These studies suggest that Twitter can be used ef-
fectively to detect breaking news before they are pub-
lished in traditional news media. To do that tweets
that can be considered news-worthy should be identi-
fied while disregarding the noisy ones. Here we use
the term noisy as the tweets which are expressing per-
sonal matters. Subsequently, for the tweets detected
in the first process, the tweets that are deemed un-
trustworthy need to be pruned. The challenges that
we consider in this paper are:
Finding a suitable topic modeling method for
Continuous training of the model as we acquire
new tweets and real-time processing of the issues
This paper concerns the issue of finding out news-
worthy tweets and seek to find a possible strategy for
collecting breaking news through Twitter using topic
modeling techniques.
The proposed work in this paper is continuing as
a part of the SmartMedia program
which was started
in 2011 at Norwegian University of Science and Tech-
nology (NTNU) in close collaboration with Scandina-
vian media industry. With this program it is targeted
to present online news in an effective and personal-
ized way to the users (Gulla et al., 2014), as well as
Wold, H., Vikre, L., Gulla, J., Özgöbek, Ö. and Su, X.
Twitter Topic Modeling for Breaking News Detection.
In Proceedings of the 12th International Conference on Web Information Systems and Technologies (WEBIST 2016) - Volume 2, pages 211-218
ISBN: 978-989-758-186-1
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
to build the context aware news expreriences based
on deep understanding of text in continuous news
streams (Ingvaldsen et al., 2015).
In Section 2 we present the previous research in
the field of topic modeling in general, and on tweets
in particular. Section 4 describes how we aim to find
a suitable topic modeling technique to handle tweets
in real time. After that, we evaluate the results of the
different topic modeling techniques in Section 5. In
Section 7, we discuss our results while Section 8 sum-
marizes our findings along with the proposal of future
A topic model of a collection of documents is a
trained statistical model that exposes abstract topics
in the collection. Each document may concern multi-
ple topics in different proportions, like a news article
about pets that may write about topics like cats, dogs,
and fish. The topics themselves are represented by
high-frequency words that occur in the descriptions
of the topics. Historically, topic modeling has been
widely used to explore topical trends in large corpora
that spans several years, analyzing for example polit-
ical and social changes in important time periods.
In recent years, topic modeling has been increas-
ingly utilized for analyzing corpora of tweets. Latent
Dirichlet Allocation (LDA) (Blei et al., 2003) is one
of the most widely used techniques for this analysis.
There are several modifications and extensions pro-
posed to LDA that improves its performance in social
media settings in general, and for tweets in particular.
In (Rosen-Zvi et al., 2004) an extension to LDA
called the Author-Topic Model (AT model) is pro-
posed. In (Rosen-Zvi et al., 2010), it is showed that
when the test documents contain only a small number
of words, the proposed model outperforms LDA. This
research was done on a collection of 1,700 NIPS con-
ference papers and 160,000 CiteSeer abstracts. The
work is done on abstracts which are shorter than nor-
mal documents but still longer than regular tweets.
(Hong and Davison, 2010) showed how training a
topic model on aggregated messages results in higher
quality learned models, yielding better results when
modeling tweets. In this work all the messages of a
particular user are aggregated before training the LDA
model on them. This simple and straightforward ex-
tension to LDA gives a more accurate topic distribu-
tion than running LDA on each tweet individually.
In (Zhao et al., 2011) an empirical comparison is
done between Twitter and the New York Times. Us-
ing an extension to LDA called Twitter-LDA is used,
they concluded that Twitter could be a good source
of news that has low coverage in other news media.
The study also suggets that while Twitter users are not
exceptionally interested in world news, they do help
spread awareness of important world events.
Another method that has been used for topic
modeling tweets is Hierarchical Dirichlet Processes
(HDP) (Teh et al., 2006). HDP is a nonparametric
Bayesian approach which is used to cluster related
data and can be used to cluster tweets that have sim-
ilar topics. (Wang et al., 2013) describes how HDP
can be used to detect events occurring from tweets in
real-time and shows how the clustering in HDP works
on these tweets.
All of these studies suggest that there are several
techniques and methods that perform well in different
areas that can be utilized for our purpose of collecting
breaking news from Twitter. Although there are stud-
ies that have experimented with similar ideas (Zhao
et al., 2011), there are few who have tried to do this
in real-time. Most studies have done this in semi real-
time or looked at previous data to see if they could get
an indication of whether or not it is possible to fetch
potential breaking news using Twitter.
When news-worthy events happen, people who wit-
ness it are often quick to post about the event to their
social network feeds in general, and to their Twitter
accounts in particular. In (Kwak et al., 2010) it was
found that any retweeted tweet reached an average of
1,000 users. This means Twitter could be an inter-
esting place for detecting breaking news as they are
emerging. Unfortunately, many of the posts on Twit-
ter are not news items, but rather address personal
opinions and mundane status updates or similar, such
as those found in figures 1 and 4. Other tweets are
more serious and potentially related to news, such as
figure 3. Lastly some tweets (e.g. figure 2) are entirely
written in foreign languages often using non-latin al-
phabets. The first step in detecting breaking news is
then to filter out the noise, leaving posts that are po-
tential news for the analysis.
The main challenge of news detection on Twitter
is the length restriction on tweets. A tweet can be a
maximum of 140 characters long. Because tweets are
Figure 1: Example of short, non-serious, tweet.
WEBIST 2016 - 12th International Conference on Web Information Systems and Technologies
Figure 2: Example of tweet written in a foreign language.
Figure 3: Example of long, serious, tweet.
Figure 4: Example of spam tweet.
so short and some posts might not directly use com-
mon keywords for describing an event, simply look-
ing for certain keywords will result in only a handful
of the actual news posts being detected. In addition,
the list of keywords would have to be updated manu-
ally as the news context evolves over time.
Instead of using fixed keywords for detecting news
on Twitter, we have focused on a few different topic
modeling techniques enabling us to dynamically find
potential news. We also combined the techniques
with a news index. These techniques allow to update
the models over time, ensuring that they stay relevant.
Topic modeling may enable us to identify a new
tweet as news-related even though we do not have an
exact list of keywords that constitute news. Moreover,
the technique has the additional benefit of classify-
ing tweets topic-wise and even associating them with
some prominent topic words that may not actually ap-
pear in every tweet of this topic. The topic words pro-
vide useful summaries and the whole structure of top-
ics associated with tweets produces a more flexible
way of grouping tweets than traditional clustering.
In this section, we introduce a set of methods for train-
ing topic models concerning Twitter. After clarifying
the theoretical aspect of the LDA and HDP models we
discuss how they can be used to topic model tweets in
4.1 Latent Dirichlet Allocation
Latent Dirichlet Allocation (LDA) is a machine learn-
ing technique that identifies latent information about
topics in large collections of documents. LDA treats
each document as a vector of word counts where each
document is a probability distribution over some top-
ics, and each topic is the probability distribution over
a number of words. For each document in the collec-
tion the LDA algorithm picks a topic according to the
multinomial distribution of words in the document. It
then uses the topic to generate the word itself accord-
ing to the topic’s multinomial distribution and repeats
these two steps for all the words in the document (Blei
et al., 2003).
LDA can be defined formally as follows:
1. A number of topics t, documents d, and a length
of a document N.
2. T distributions over the vocabulary where β
is the
distribution over words in topic t.
3. D distributions over topics where θ
is the distri-
bution of topics in document d.
4. The topic given for document d is z
, and z
the assignment of a topic to the nth word in docu-
ment d.
5. The observed words in document d are given as
, where w
is the nth word in document d.
This gives us the formula (Blei, 2012) :
LDA has become one of the “state-of-the-art”
topic modeling algorithms in the later years after it
was presented in (Blei et al., 2003). The algorithm
uses the “bag-of-words” principle to represent the
documents, which is satisfactory when dealing with
larger documents where it is possible to explore co-
occurrences at the document level. With this it is pos-
sible to achieve a clear overview of all topics related
to a document (Titov and McDonald, 2008), which in
many cases is the ideal result.
The LDA model has been shown to work fairly
well when topic-modeling tweets. Some studies, how-
ever, suggest that slight modifications to the LDA
model, such as the AT model (Hong and Davison,
2010) and Twitter-LDA (Zhao et al., 2011) perform
even better. One reason for this is the document
length, which has a fairly large impact on the outcome
of the topic modeling result.
Online LDA is a variation of LDA in which the
model can be updated on the fly. As we are interested
in topic modeling of a real-time stream of tweets, tra-
ditional LDA may not be sufficient for our purposes.
For this reason, we have chosen to use Online LDA.
So by using this model it is possible to update and
Twitter Topic Modeling for Breaking News Detection
continuously build upon the already existing model,
which is based on the information that comes from
the data stream. In our case that means new docu-
ments (that is tweets) will be added to the model con-
tinuously as they are received by the system.
4.2 Author-topic
Author-topic (AT) model is an extension of LDA. In
this model, the content of each document and the in-
terests of authors are simultaneously modeled. AT
uses probabilistic “topics-to-author” model, which al-
lows a mixture of different weights (θ and φ) for dif-
ferent topics to be determined by authors of a spe-
cific document (Rosen-Zvi et al., 2004). φ is gener-
ated similar to θ, where it is chosen from a symmetric
The AT-model as defined above, would result in a
very large memory footprint if directly implemented.
The reason for this is that we would have to store all
inbound tweets to be able to continuously update the
documents written by each author, which would scale
linearly in time. So instead of implementing it di-
rectly we replicate the approach of (Hong and Davi-
son, 2010). This involves aggregating all the docu-
ments made by a single author into one document. In
our case this means concatenating all the tweets of a
unique user into one document.
4.3 Hierarchical Dirichlet Process
The Hierarchical Dirichlet Process (HDP) is a topic
modeling technique for performing unsupervised
analysis of grouped data. HDP provides a nonpara-
metric topic model where documents are grouped by
observed words, topics are distributions over several
terms, and every document shows different distribu-
tions of topics. Its formal definition, due to (Teh et al.,
2006) is:
|γ, H DP(γ, H)
, G
, G
for each group j. Here G
is the random probabil-
ity measure of group j and its distribution is given by
a Dirichlet process. This Dirichlet process depends
on α
, the concentration parameter associated with
the group, and G
. G
is the base distribution shared
across all groups. This base distribution is given by a
Dirichlet process as well, where γ is the base concen-
tration parameter associated with the group and H is
the base distribution which governs the a priori distri-
bution over data items.
One limitation of standard HDP is that it has to
crawl through all the existing data multiple times.
Therefore (Wang et al., 2011) proposed a variant of
HDP which they called Online Hierarchical Dirich-
let Process. The Online-HDP is designed to analyze
streams of data. It provides the flexibility of HDP
and the speed of online variational Bayes. The on-
line aspect of the Online-HDP is both a performance
improvement and a variation in how models are up-
dated. The improvement in performance is achieved
by not having to crawl through the entire corpus re-
peatedly. Instead of having several passes with a fixed
set of data, it was suggested by (Wang et al., 2011) the
updates of the model can be optimized by iteratively
choosing a random subset of the data, and then up-
dating the variational parameters based on the current
subset of data.
In the experiment part of this work, we utilize a set of
tweets to train a topic model using the LDA and HDP
models. We train both models by treating each tweet
as a separate document and also by aggregating all
tweets of a single user into one document. This gives
us four different approaches to compare. Our moti-
vation for training the models in two different ways
using the same dataset is to see if we can counter the
biggest problem with modeling tweets which is their
short length. As this aggregation of tweets is an at-
tempt to emulate the AT-model, we refer to the exper-
iments using this dataset as LDA-AT and HDP-AT.
Before briefly describing the pre-processing steps
and training of the models, we describe the data set
and the data collection process. After that, we test the
various methods described in the previous section on
a set of test tweets, separate from the training tweets.
5.1 Data Set
For our experiments, we have fetched data from Twit-
ter’s own streaming API
and built a separate data set.
Our training data set contains approximately 600,000
tweets from the New York (USA) area, collected in
the period from November 26, 2014 to December 5,
Using Twitter’s API it is possible to get an exten-
sive set of metadata connected to each tweet. Most of
this metadata is of no significance for topic modeling,
and is stripped away. For the purposes of the experi-
ment, the only things we keep are the screen name of
the author and the content of the tweet itself.
WEBIST 2016 - 12th International Conference on Web Information Systems and Technologies
Based on an idea from Meyer et al. (Meyer et al.,
2011), we ignored any tweet containing non-ASCII
characters. The rationale behind this idea is that there
are several tweets containing nothing but emoticons,
as well as several tweets written using non-ASCII
characters (such as Arabic and Chinese). The dan-
ger of doing this is that a few relevant tweets might
also be removed. We have nevertheless decided this
is a fair tradeoff and negligable in order to find the
majority of the news including tweets. If we were
to include tweets made in foreign languages using a
non-ASCII alphabet, the complexity of the analysis
would dramatically increase. Moreover we are inter-
ested in presenting breaking news to a user who, pre-
sumably, knows English but not necessarily other lan-
guages. Another aspect of ignoring non-ASCII char-
acters is that tweets containing unicode emoticons are
mostly status updates or chatter, which can be safely
ignored. Finally, if an actual news post gets filtered
out due to including non-ASCII characters, chances
are high that someone else will have posted about this
same event without utilizing non-ASCII characters.
All these points considered, we decided that it was
a fair tradeoff to ignore all the tweets containing non-
ASCII characters. In our data set around 30% of the
tweets are ignored due to their inclusion of non-ASCII
Other than removing tweets containing non-
ASCII characters, we have replaced all the URLs with
the word “LINK”. We kept all hashtags but we re-
moved words that are not meaningful like combina-
tion of many words prepared as a hashtag but lacks
the ’#’ sign. We also removed common stop words
since they have no effect on analyzing the meaning
of tweets by using topic modeling. We have not per-
formed any other word processing on the tweets or
stemming on the words. We also made a copy of
the data set where all the tweets belonging to a user
merged together.
Before performing the experiment, we trained the
different algorithms on the dataset described above.
We used the Python library Gensim
as it has embed-
ded support for both LDA and HDP. For both models,
the number of topics were set to 50. This number was
chosen based on earlier research done by Hong and
Davison (Hong and Davison, 2010) which showed
that the best results were given if the number of topic
were set to 50. There are some additional parameters
that can be tweaked for the models, and we tried a
few different settings. First we made the LDA model
update in chunks of 1000. This means that the model
is trained with 1000 tweets at a time. For the training
set where each user’s messages are aggregated, this
meant that the model was updated in chunks of 1000
unique users. By doing so in our experiments, we
observed that this caused one topic to be “inflated”.
Almost every single message we attempted to clas-
sify using a model trained in this fashion was classi-
fied into the same, highly general topic. By increasing
the chunk size by a factor of ten to 10,000, this phe-
nomenon disappeared.
Furthermore, we set the number of passes to per-
form with LDA to 10. This was chosen to ensure
the initial training set converged well on topics. For
HDP, we set the chunk size to 256. When doing on-
line training (meaning that updating the model with
real-time tweets), it is not possible to change these
parameters. So the model is simply updated straight
away with the provided corpus (new messages re-
ceived in real-time). It is somewhat possible to ad-
just the chunk size with real-time messages by doing
batch updates. The size of the batches will have to
be balanced around not being too rare in addition to
not being too small so that they skew the models. A
compromise here from our trials seem to be update at
about every 500-1000 new tweets that arrive.
After training the models, we used them on a set
of 100 tweets collected in the same time period as the
testing set. These tweets were new in that they were
not part of the training set.
Additionally we used the models on portions of
the New York Times annotated corpus (Sandhaus,
2008) to assess which topics were most frequently
found in actual news articles. We did this by tally-
ing up the most relevant topic for each of the articles
in the corpus. Doing this gave us a list over the top-
ics most related to the articles in the New York Times
corpus. The “score” of a topic, then, is the number
of articles in the New York Times corpus that topic
was the best fit for. This was done as a part of fil-
tering tweets concerning news and not as part of the
experiment described below.
5.2 Experiment
To conduct our experiment we asked 7 people to par-
ticipate for manual grading of our results. As men-
tioned above, we collected 100 tweets for our exper-
iment. We utilized each of the trained models (LDA,
LDA-AT, HDP, HDP-AT) to topic model these new
tweets. The results of this topic modeling was dis-
tributed to our participants where they graded how
well the topic assigned fit on a scale from 1-5. Here a
score of 1 means the topic is not relevant, and 5 means
it is a perfect fit.
As shown in table 1, some of the resulting topics are
very generic and cover a broad set of terms. This is
Twitter Topic Modeling for Breaking News Detection
Table 1: An example of words in topic #32 and #1 found by
Topic #32
good 0.0216
love 0.0207
time 0.0205
day 0.0188
today 0.0150
night 0.0124
great 0.0103
work 0.0099
life 0.0093
youre 0.0089
Topic #1
game 0.0649
played 0.0490
team 0.0354
win 0.0345
play 0.0253
football 0.0147
games 0.0145
ball 0.0114
pick 0.0112
points 0.0102
Figure 5: Results from modeling 100 tweets using LDA,
not surprising, as many tweets are about the every-
day affairs of their authors. As we collected tweets
from the New York area we expected an abundance
of tweets about things occurring there.
Figure 5 shows the scores given to the categorization
of each tweet in our test set by our test subjects. As
is evident there is a clear difference in performance
between the four methods. The two HDP variants,
HDP-AT in particular performed rather poorly than
HDP having an average score of 1.792, and HDP-AT
having an average score of 1.178. The LDA variants
performed much better compared to HDP methods.
LDA had an average score of 2.000 where LDA-AT
had an average score of 2.610 given by the partici-
Table 2: Precision results given by the different topic mod-
eling methods.
Method Precision
LDA 0.287
LDA-AT 0.520
HDP 0.059
HDP-AT 0.198
pants of the experiment.
To measure the results and give a final score of
the different methods, we use precision. We set the
threshold for a topic being relevant for a tweet at 3 out
of 5. This means that every assignment with a score of
3 or higher, gets marked as relevant, while any assign-
ment graded 2 or lower gets marked as not relevant.
We calculate the precision by taking the number of
relevant assignments and dividing them by the total
number of assignments.
Using the scores given by the test participants and
the definition above, we calculated the precision of
each method. These scores can be found in table 2.
As mentioned previously, the sparsity and noisy na-
ture of tweets make it difficult to get reasonable data
out of them. This is especially valid when we strip
them of stop words. After this process some tweets
end up with a very low word count, and as such is
not likely to get a suitable topic assigned. Further-
more, tweets in foreign languages are a challenge, and
is not something we have taken into account in this
work. Even when ignoring tweets containing non-
ASCII characters, some languages (such as Spanish)
still slip through.
As the results show, LDA-AT outperforms the
other three models. The main rationale behind this
is that by combining all tweets from a single author in
the training set into a document (meaning the train-
ing set then contains one document per author), it
becomes possible to somewhat counter the document
length limitation inherent to tweets. As authors tend
to stick to only a handful of topics, this should not
skew the model in any meaningful way.
To be able to filter out what is news on Twitter, we
utilized the New York Times annotated corpus, as de-
scribed earlier. We modeled all the articles published
by the New York Times in 2006 using the LDA-AT
approach. We chose that approach, as our previous
experiment had suggested it had the best performance
on tweets of the four approaches tested. This gave us a
WEBIST 2016 - 12th International Conference on Web Information Systems and Technologies
Table 3: Test results showing the number of relevant and
non-relevant tweets with their precision values in different
#Categories Relevant Not rel. Precision
3 categories 7 247 0.028
2 categories 7 101 0.065
1 category 4 35 0.103
I’m on grand jury watch in the Eric Gar-
ner case. They are meeting and could de-
cide today. Follow here and @NYTMetro for
breaking updates
Figure 6: An example of a news tweet found by using the
topic modeling method combined with the news index.
handful of topics that were much more likely to be as-
signed to actual news articles than others. We then ran
some fresh tweets through the model, and those who
assigned to one of the top ranked topics are saved to
see if they were actually news items. We did this three
times; one with the top 3 topics, one with the top 2,
and finally one with only the top topic. The results
can be seen in table 3.
As is immediately clear, our approach for extract-
ing news does not perform very well. The data set
tested consisted of 3,454 tweets, meaning more than
90% of the total tweets were removed. Even so the
best precision achieved was merely 0.103.
In this work we experimented different methods for
detecting breaking news from Twitter streams. As
a result of our comparison of different methods for
topic modeling tweets, it seems using an LDA model
coupled with aggregating all the tweets of a user,
called LDA-AT, is the most effective approach. The
largest limitation of using only topic modeling for
news detection, is that it is sometimes difficult to
know what a topic represents. Another pervasive
limitation is that of Twitter’s 140 characters limit on
Using the LDA-AT approach is a challenge when
working with streams of real-time tweets. In a real-
time setting the goal is to model tweets as they ar-
rive. As our results show, combining all the tweets
of a single user into one document is desired when
performing this. In a real-time setting, however, one
cannot afford to wait for tweets for an extended pe-
riod of time to make sure that each user’s document is
large enough before updating the model. This is be-
cause, that would compromise the goal of topic mod-
eling the tweets as soon as they arrive. One potential
solution to this for news detection purposes would be
to use a static topic model. On the other hand, this
have the risk of the model getting outdated as popu-
lar topics on Twitter drift. Another potential solution
for the real-time processing is to incrementally update
the model in a set time, so as not to overly delay the
topic modeling of the tweets themselves. This solu-
tion comes with another issue, however. The topic
model we have used does not allow for terms to be
added to the dictionary after the model has been ini-
tialized. This can be alleviated by using a hash map
based dictionary, with the caveat that certain terms
will share the same index and potentially lowers the
precision of the model. The alternative is to keep the
dictionary static. The danger of doing this is that over
time certain terms that are not in the dictionary could
become important to identify news.
However, the inherent limitation of 140 words per
tweet is problematic for any statistical model that
draws on word frequencies. Only a few content-
relevant nominal phrases are included, and most of
these 140 words tend to come from the stop word list.
Even though the approach may be improved some-
what, the experiments seem to suggest that topic mod-
eling is far from being effective in detecting breaking
news on Twitter in the near future. Other and sim-
pler techniques, like detecting clusters of tweets at
particular locations at particular times, may be both
computationally more efficient and quality-wise more
On the other hand, topic modeling has some other
advantages that may be interesting as part of a larger
news aggregator service. Associating tweets with
multiple topics, we can organize news tweets accord-
ing to multiple dimensions for easier user inspection.
We may also use the most prominent words of each
topic representation as a short summary or title of
a group of related tweets, making it unnecessary to
check every tweet to understand the overall content.
In this paper, we have compared four different meth-
ods for topic modeling tweets as part of a breaking
news detection system. As a result of our experiments
LDA-AT outperforms the other three models. We also
attempted to use the trained topic model in a practi-
cal manner to detect news. We used the New York
Times annotated corpus to decide which topics were
most likely to be assigned to news articles, before us-
Twitter Topic Modeling for Breaking News Detection
ing those topics to filter a new data set. The results
of this experiment show that the majority of tweets
fetched using this method is non news, achieving a
precision of only 0.103 in the best case.
Topic modeling itself is not likely to be sufficient
for detecting breaking news from Twitter. The tweets
are too short and too ambiguous to generate statistical
models of the necessary precision. As a supplement
to other techniques for news detection, they may how-
ever be useful, since they assume no knowledge of
location, time or author.
From a news aggregator perspective topic model-
ing is interesting also for clustering and summarizing
news content. Each tweet is associated with a number
of relevant topics or clusters, and each topic is again
described using a set of prominent word for that topic.
In the future we intend to further explore the cluster-
ing abilities of topic modeling to improve the user ex-
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