@Proceedings{proc_microposts2014@www2014,
  title = {Proceedings, 4th Workshop on Making Sense of Microposts {(\#Microposts2014)}: Big things come in small packages, Seoul, Korea, 7th April 2014},
  year = 2014,
  booktitle = {Making Sense of Microposts {(\#Microposts2014)}},
  editor = {Matthew Rowe and Milan Stankovic and Aba-Sah Dadzie},
  month = {April},
  url = {http://ceur-ws.org/Vol-1141},
}

@InProceedings{microposts2014_strohmaier:2014,
  author = {Markus Strohmaier},
  title = {Computational Social Science and Microblogs -- The Good, the Bad and the Ugly},
  crossref = {proc_microposts2014@www2014},
  pages = {1--1},
  booktitle = {4th Workshop on Making Sense of Microposts {(\#Microposts2014)}},
  year = 2014,
  keywords = {Social data, computational social science, social behavior, web science, online social networks},
  url = {http://ceur-ws.org/Vol-1141/keynote_abstract.pdf},
}

@InProceedings{microposts2014_panisson.ea:2014,
  author = {Andr\'e Panisson and Laetitia Gauvin and Marco Quaggiotto and Ciro Cattuto},
  title = {Mining Concurrent Topical Activity in Microblog Streams},
  crossref = {proc_microposts2014@www2014},
  pages = {3--10},
  booktitle = {4th Workshop on Making Sense of Microposts {(\#Microposts2014)}},
  year = 2014,
  abstract = {exhibit patterns of collective attention across diverse topics, with temporal structures determined both by exogenous factors
and endogenous factors. Teasing apart different topics and resolving their individual, concurrent, activity timelines is a key challenge in extracting knowledge from microblog streams. Facing this challenge requires the use of methods that expose latent signals by using term correlations across posts and over time.
Here we focus on content posted to Twitter during the London 2012 Olympics, for which a detailed schedule of events is independently available and can be used for reference. We mine the temporal structure of topical activity by using two methods based on non-negative matrix factorization.
We show that for events in the Olympics schedule that can be semantically matched to Twitter topics, the extracted Twitter activity timeline closely matches the known timeline from the schedule.
Our results show that, given appropriate techniques to detect latent signals, Twitter can be used as a social sensor to extract topical-temporal information on real-world events at high temporal resolution.},
  keywords = {topic detection, microblogs, matrix and tensor factorization, collective attention, event detection},
  url = {http://ceur-ws.org/Vol-1141/paper_04.pdf},
}

@InProceedings{microposts2014_prapulag.ea:2014,
  author = {Prapula G and Soujanya Lanka and Kamalakar Karlapalem},
  title = {{TEA}: Episode Analytics on Short Messages},
  crossref = {proc_microposts2014@www2014},
  pages = {11--18},
  booktitle = {4th Workshop on Making Sense of Microposts {(\#Microposts2014)}},
  year = 2014,
  abstract = {Twitter is a widely used micro-blogging service, which in recent times, has become a reliable source of happening news around the world [11]. Breaking news are covered in twitter; the magnitude and volumes of tweets reflecting on the nature and intensity of the news. During events, many tweets are posted either expressing sentiments about the event or just about the occurrence of the event. Events related to an entity that have attracted a large number of tweets can be considered significant in the entity's twitter lifetime. Entity could represent a person, movie, community, electronic gadgets, software products and like wise.In this work, we attempt to automatically detect significant events related to an entity. An episode, is an event of importance; identified by processing the volumes of tweets/posts in a short time.	
The key features implemented in Tweet Episode Analytics (TEA) system are:
(i) detecting episodes among the streaming tweets related to a given entity over a period of time (from the entity's birth i.e., mention in the tweet world till date), (ii) providing visual analytics (like sentiment scoring and frequency of tweets over time) of each episode through graphical interpretation.},
  keywords = {Tweets, Episode, Text Analytics},
  url = {http://ceur-ws.org/Vol-1141/paper_08.pdf},
}

@InProceedings{microposts2014_cambria.ea:2014,
  author = {Erik Cambria and Soujanya Poria and Alexander Gelbukh and Kenneth Kwok},
  title = {{Sentic API}: A Common-Sense Based {API} for Concept-Level Sentiment Analysis},
  crossref = {proc_microposts2014@www2014},
  pages = {19--24},
  booktitle = {4th Workshop on Making Sense of Microposts {(\#Microposts2014)}},
  year = 2014,
  abstract = {The bag-of-concepts model can represent semantics associated with natural language text much better than bags-of-words. In the bag-of-words model, in fact, a concept such as cloud_computing would be split into two separate words, disrupting the semantics of the input sentence. Working at concept-level is important for tasks such as opinion mining, especially in the case of microblogging analysis. In this work, we present Sentic API, a common-sense based application programming interface for concept-level sentiment analysis, which provides semantics and sentics (that is, denotative and connotative information) associated with 15,000 natural language concepts.},
  keywords = {Natural language processing; Sentiment analysis},
  url = {http://ceur-ws.org/Vol-1141/paper_02.pdf},
}

@InProceedings{microposts2014_schulz.ea:2014,
  author = {Axel Schulz and Eneldo {Loza Menc\'ia} and Thanh Tung Dang and Benedikt Schmidt},
  title = {Evaluating Multi-label Classification of Incident-related Tweets},
  crossref = {proc_microposts2014@www2014},
  pages = {26--33},
  booktitle = {4th Workshop on Making Sense of Microposts {(\#Microposts2014)}},
  year = 2014,
  abstract = {Microblogs are an important source of information in emergency management as lots of situational information is shared, both by citizens and official sources. It has been shown that incident-related information can be identified in the huge amount of available information using machine learning. Nevertheless, the currently used classification techniques only assign a single label to a micropost, resulting in a loss of important information that would be valuable for crisis management.
With this paper we contribute the first in-depth analysis of multi-label classification of incident-related tweets. We present an approach assigning multiple labels to these messages, providing additional information about the situation at-hand. An evaluation shows that multi-label classification is applicable for detecting multiple labels with an exact match of 84.35\%. Thus, it is a valuable means for classifying incident-related tweets. Furthermore, we show that correlation between labels can be taken into account for these kinds of classification tasks.},
  keywords = {Microblogs, Multi-label Learning,  Social Media},
  url = {http://ceur-ws.org/Vol-1141/paper_01.pdf},
}

@InProceedings{microposts2014_dlugolinsky.ea:2014,
  author = {\v{S}tefan Dlugolinsk\'y and Peter Krammer and Marek Ciglan and Michal Laclav\'ik and Ladislav Hluch\'y},
  title = {Combining Named Entity Recognition Methods for Concept Extraction in {Microposts}},
  crossref = {proc_microposts2014@www2014},
  pages = {34--41},
  booktitle = {4th Workshop on Making Sense of Microposts {(\#Microposts2014)}},
  year = 2014,
  abstract = {NER in microposts is a key and challenging task of mining semantics from social media. Our evaluation of a number of popular NE recognizers over a micropost dataset has shown a significant drop-off in results quality. Current state-of-the-art NER methods perform much better on formal text than on microposts. However, the experiment provided us with an interesting observation -- although individual NER tools did not perform very well on micropost data, we have received recall over 90\% when we merged all the results of the examined tools. This means that if we would be able to combine different NE recognizers in a meaningful way, we might be able to get NER in microposts of an acceptable quality. In this paper, we propose a method for NER in microposts, which is designed to combine annotations yielded by existing NER tools in order to produce more precise results than input tools alone. We combine NE recognizers utilizing ML
techniques, namely decision tree and random forest using the C4.5 algorithm. The main advantage of the proposed method lies in the possibility of combining arbitrary NER methods and in its application on short, informal texts. The evaluation on a standard dataset shows that the proposed approach outperforms underlying NER methods as well as a baseline recognizer, which is a simple combination of the best underlying recognizers for each target NE class. To the best of our knowledge, up-to-date, the proposed approach achieves the highest $F_1$ score on the \#MSM2013 dataset.},
  keywords = {named entity recognition, machine learning, microposts},
  url = {http://ceur-ws.org/Vol-1141/paper_09.pdf},
}

@InProceedings{microposts2014_bellaachia.ea:2014,
  author = {Abdelghani Bellaachia and Mohammed Al-Dhelaan},
  title = {{HG-Rank}: A Hypergraph-based Keyphrase Extraction for Short Documents in Dynamic Genre},
  crossref = {proc_microposts2014@www2014},
  pages = {42--49},
  booktitle = {4th Workshop on Making Sense of Microposts {(\#Microposts2014)}},
  year = 2014,
  abstract = {Conventional keyphrase extraction algorithms are applied to a fixed corpus of lengthy documents where keyphrases distinguish documents from each other. However, with the emergence of social networks and microblogs, the nature of such documents has changed. Documents are now of short length and evolve topics which require specific algorithms to capture all features. In this paper, we propose a hypergraph-based ranking algorithm that models all the features in a random walk approach. Our random walk approach uses weights of both hyperedges and vertices to model short documents' temporal and social features, as well as discriminative weights for word features respectively, while measuring the centrality of words in the hypergraph. We empirically test the effectiveness of our approach in two different data sets of short documents and show that our approach has an improvement of 14\% to 25\% in precision over the closest baseline in a Twitter data set and 10\% to 27\% in the Opinosis data set.},
  keywords = {Text hypergraphs; Keyphrase extraction; Random walks; Short documents; Hypergraph random walks},
  url = {http://ceur-ws.org/Vol-1141/paper_06.pdf},
}

@InProceedings{microposts2014_sinha.ea:2014,
  author = {Priyanka Sinha and Anirban Dutta Choudhury and Amit Kumar Agrawal},
  title = {Sentiment Analysis of {Wimbledon} Tweets},
  crossref = {proc_microposts2014@www2014},
  pages = {51--52},
  booktitle = {4th Workshop on Making Sense of Microposts {(\#Microposts2014)}},
  year = 2014,
  abstract = {Annotating videos in the absence of textual metadata is a major challenge as it involves complex image and video analytics, which is often error prone. However, if the video is a live coverage of an event, time correlated textual feed about the same event can act as a valuable source of aid for such annotation. Popular real time microblog streams like Twitter feeds can be an ideal source of such textual information. In this paper we explore the possibility of such correlation with the sentiment analysis of a set of tweets of the Roger Federer and Novak ``Nole'' Djokovic semi finals match at Wimbledon 2012.},
  keywords = {Twitter, Wimbeldon, Sentiment Analysis, TV},
  url = {http://ceur-ws.org/Vol-1141/paper_10.pdf},
}


@InProceedings{microposts2014_neel_cano.ea:2014,
  author = {Amparo Elizabeth {Cano Basave} and Giuseppe Rizzo and Andrea Varga and Matthew Rowe and Milan Stankovic and Aba-Sah Dadzie},
  title = {Making Sense of Microposts ({\#Microposts2014}) Named Entity Extraction \& Linking Challenge},
  crossref = {proc_microposts2014@www2014},
  pages = {54--60},
  booktitle = {4th Workshop on Making Sense of Microposts {(\#Microposts2014)}},
  year = 2014,
  abstract = {Microposts are small fragments of social media content and a popular medium for sharing facts, opinions and emotions. They comprise a wealth of data which is increasing exponentially, and which therefore presents new challenges for the information extraction community, among others. This paper describes the `Making Sense of Microposts' (\#Microposts2014) Workshop's Named Entity Extraction and Linking (NEEL) Challenge, held as part of the 2014 World Wide Web conference (WWW'14). The task of this challenge consists of the automatic extraction and linkage of entities appearing within English Microposts on Twitter. 
Participants were set the task of engineering a named entity extraction and DBpedia linkage system targeting a predefined taxonomy, to be run on the challenge data set, comprising a manually annotated training and a test corpus of Microposts. 43 research groups expressed intent to participate in the challenge, of which 24 signed the agreement required to be given a copy of the training and test datasets. 8 groups fulfilled all submission requirements, out of which 4 were accepted for the presentation at the workshop and a further 2 as posters. The submissions covered sequential and joint methods for approaching the named entity extraction and entity linking tasks. We describe the evaluation process and discuss the performance of the different approaches to the \#Microposts2014 NEEL Challenge. },
  keywords = {Microposts, Named Entity, Evaluation, Extraction, Linking, Disambiguation, Challenge},
  url = {http://ceur-ws.org/Vol-1141/microposts2014_neel-challenge-report.pdf},
}

@InProceedings{microposts2014_neel_chang.ea:2014,
  author = {Ming-Wei Chang and Bo-June Hsu and Hao Ma and Ricky Loynd and Kuansan Wang},
  title = {{E2E}: An End-to-End Entity Linking System for Short and Noisy Text},
  crossref = {proc_microposts2014@www2014},
  pages = {62--63},
  booktitle = {4th Workshop on Making Sense of Microposts {(\#Microposts2014)}},
  year = 2014,
  abstract = {We present E2E, an end-to-end entity linking system that is designed for short and noisy text found in microblogs and text messages. Mining and extracting entities from short text is an essential step for many content analysis applications. By jointly optimizing entity recognition and disambiguation as a single task, our system can process short and noisy text robustly.},
  keywords = {Information Extraction, Social Media, Entity Linking},
  url = {http://ceur-ws.org/Vol-1141/paper_18.pdf},
}

@InProceedings{microposts2014_neel_habib.ea:2014,
  author = {Mena B. Habib and Maurice van Keule and Zhemin Zhu},
  title = {Named Entity Extraction and Linking Challenge: University of Twente at {\#Microposts2014}},
  crossref = {proc_microposts2014@www2014},
  pages = {64--65},
  booktitle = {4th Workshop on Making Sense of Microposts {(\#Microposts2014)}},
  year = 2014,
  abstract = {Twitter is a potentially rich source of continuously and instantly updated information.
Shortness and informality of tweets are challenges for Natural Language Processing
(NLP) tasks. In this paper we present a hybrid approach for Named Entity Extraction (NEE) and Linking (NEL) for tweets. Although NEE and NEL are two topics that are well studied in literature, almost all approaches treated the two problems separately. We believe that disambiguation (linking) could help improving the extraction process. We call this potential for mutual improvement, the reinforcement effect. It mimics the way humans understand natural language. Furthermore, our proposed approaches handles uncertainties involved in the two processes by considering possible alternatives.},
  keywords = {Named Entity Extraction, Named Entity Linking, Social Media Analysis, Twitter Messages},
  url = {http://ceur-ws.org/Vol-1141/paper_13.pdf},
}

@InProceedings{microposts2014_neel_scaiella.ea:2014,
  author = {Ugo Scaiella and Michele Barbera and Stefano Parmesan and Gaetano Prestia and Emilio Del Tessandoro and Mario Ver\`i},
  title = {{DataTXT} at {\#Microposts2014 Challenge}},
  crossref = {proc_microposts2014@www2014},
  pages = {66--67},
  booktitle = {4th Workshop on Making Sense of Microposts {(\#Microposts2014)}},
  year = 2014,
  abstract = {In this paper we describe the approach taken for the ``Making Sense of Microposts challenge 2014" (\#Microposts2014), where participants were asked to cross reference micro-posts extracted from Twitter with DBpedia URIs belonging to a given taxonomy. 
For this task we deployed \textsc{dataTXT} which is the evolution of [3], the state-of-the-art topic annotator for short texts and which has proven to be very effective and efficient in several challenging scenarios [2].},
  keywords = {topic annotator, entity extraction, datatxt},
  url = {http://ceur-ws.org/Vol-1141/paper_19.pdf},
}

@InProceedings{microposts2014_neel_yosef.ea:2014,
  author = {Mohamed Amir Yosef and Johannes Hoffart and Yusra Ibrahim and Artem Boldyrev and Gerhard Weikum},
  title = {Adapting {AIDA} for Tweets},
  crossref = {proc_microposts2014@www2014},
  pages = {68--69},
  booktitle = {4th Workshop on Making Sense of Microposts {(\#Microposts2014)}},
  year = 2014,
  abstract = {This paper presents our system for the ``Making Sense of Microposts 2014 (\#Microposts2014)'' challenge. Our system is based on AIDA, an existing system that links entity mentions in natural language text to their corresponding canonical entities in a knowledge base (KB). AIDA collectively exploits the prominence of entities, contextual similarities, and coherence to effectively disambiguate entity mentions. The system was originally developed for clean and well-structured text (e.\,g.\ news articles). We adapt it for microposts, specifically tweets, with special focus on the named entity recognition and the entity candidate lookup.},
  keywords = {Entity Recognition, Entity Disambiguation, Social Media},
  url = {http://ceur-ws.org/Vol-1141/paper_15.pdf},
}

@InProceedings{microposts2014_neel_bansal.ea:2014,
  author = {Romil Bansal and Sandeep Panem and Priya Radhakrishnan and Manish Gupta and Vasudeva Varma},
  title = {Linking Entities in {\#Microposts}},
  crossref = {proc_microposts2014@www2014},
  pages = {71--72},
  booktitle = {4th Workshop on Making Sense of Microposts {(\#Microposts2014)}},
  year = 2014,
  abstract = {Social media has emerged to be an important source of information. Entity linking in social media provides an effective way to extract useful information from microposts shared by the users. Entity linking in microposts is a difficult task as they lack sufficient context to disambiguate the entity mentions. In this paper, we do entity linking by first identifying entity mentions and then disambiguating the mentions based on three different features: (1) similarity between the mention and the corresponding Wikipedia entity pages; (2) similarity between the mention and the tweet text with the anchor text strings across multiple webpages, and  (3) popularity of the entity on Twitter at the time of disambiguation. The system is tested on the manually annotated dataset provided by Named Entity Extraction and Linking (NEEL) Challenge 2014, and the obtained results are on par with the state-of-the-art methods.},
  keywords = {Named Entity Extraction and Linking (NEEL) Challenge, Entity Linking, Entity Disambiguation, Social Media},
  url = {http://ceur-ws.org/Vol-1141/paper_16.pdf},
}

@InProceedings{microposts2014_neel_dahlmeier.ea:2014,
  author = {Daniel Dahlmeier and Naveen Nandan and Wang Ting},
  title = {Part-of-Speech is (almost) enough: {SAP Research \& Innovation}
  at the {\#Microposts2014 NEEL Challenge}},
  crossref = {proc_microposts2014@www2014},
  pages = {73--74},
  booktitle = {4th Workshop on Making Sense of Microposts {(\#Microposts2014)}},
  year = 2014,
  abstract = {This paper describes the submission of the SAP Research \& Innovation team at the \#Microposts2014 NEEL Challenge. We use a two-stage
approach for named entity extraction and linking, based on conditional
random fields and an ensemble of search APIs and rules,
respectively. A surprising result of our work is that part-of-speech
tags alone are almost sufficient for entity extraction. Our results for
the combined extraction and linking task on a development and test
split of the training set are 34.6\% and 37.2\% F$_{1}$ score,
respectively, and for the test set is 37\%.},
  keywords = {Conditional Random Field, Entity Extraction, DBpedia Linking},
  url = {http://ceur-ws.org/Vol-1141/paper_20.pdf},
}
