@Proceedings{proc_microposts2016@www2016,
  title = {Proceedings, 6th Workshop on Making Sense of Microposts {(\#Microposts2016)}: Big things come in small packages, Montr\'eal, Canada, 11th of Apr 2016},
  year = 2016,
  booktitle = {Making Sense of Microposts {(\#Microposts2016)}},
  editor = {Daniel Preo\c{t}iuc-Pietro and Danica Radovanovi\'c and Amparo E. Cano-Basave and Katrin Weller and Aba-Sah Dadzie},
  url = {http://ceur-ws.org/Vol-1691},
}

@InProceedings{microposts2016_invited_talk_grbovic.ea:2016,
  author = {Mihajlo Grbovic and Vladan Radosavljevic and Nemanja Djuric and Narayan Bhamidipati and Ananth Nagarajan},
  title = {Leveraging Blogging Activity on Tumblr to Infer Demographics and Interests of Users for Advertising Purposes},
  crossref = {proc_microposts2016@www2016},
  pages = {2--11},
  booktitle = {6th Workshop on Making Sense of Microposts {(\#Microposts2016)}},
  year = 2016,
  abstract = {As one of the leading platforms for creative content, Tumblr offers advertisers a unique way of creating brand identity. Advertisers can tell their story through images, animation, text, music, video and more, and they can promote that content by sponsoring it to appear as an advertisement in the streams of Tumblr users. In this paper, we present a framework that enabled one of the key targeted advertising components for Tumblr, specifically, gender and interest targeting. We describe the main challenges involved in the development of the framework, which include the creation of a ground truth for training gender prediction models, as well as mapping Tumblr content to an interest taxonomy. For purposes of inferring user interests, we propose a novel semi-supervised neural language model for categorization of Tumblr content (i.e., post tags and post keywords). The model was trained on a large-scale data set consisting of $6.8$ billion user posts, with a very limited amount of categorized keywords, and was shown to have superior performance over the baseline models. We successfully deployed gender and interest targeting capability in Yahoo production systems, delivering inference for users that covers more than $90$\% of the daily activities on Tumblr. Online performance results indicate advantages of the proposed approach, where we observed a $20\%$ increase in user engagement with sponsored posts in comparison to untargeted campaigns.},
  keywords = {data mining, computational advertising, audience modeling, algorithms},
  url = {http://ceur-ws.org/Vol-1691/paper_15.pdf},
}

@InProceedings{microposts2016_lim.ea:2016,
  author = {Wee Yong Lim and Mong Li Lee and Wynne Hsu},
  title = {ClaimFinder: A Framework for Identifying Claims in Microblogs},
  crossref = {proc_microposts2016@www2016},
  pages = {13--20},
  booktitle = {6th Workshop on Making Sense of Microposts {(\#Microposts2016)}},
  year = 2016,
  abstract = {Twitter is a microblogging platform that allows users to post public short messages. Posts shared by users pertaining to real-world events or themes can provide a rich ``on-the-ground'' live update of the events for the benefit of everyone. Unfortunately, the posted information may not be all credible and rumours can spread over this platform. Existing credibility assessment work have focused on identifying features for discriminating the credibility of messages at the tweet level.
However, they do not handle tweets  that contain multiple pieces of information, each of which may have different level of credibility. 
In this work, we introduce the notion of a claim based on subject and predicate terms, and propose a framework to identify claims from a corpus of tweets related to some major event  or theme. 
Specifically, we draw upon work done in open information extraction  to extract from tweets, tuples that comprises of subjects and their predicate. Then we cluster these tuples to identify claims such that each claim refers to only one aspect of the event. 
Tweets corresponding to the tuples in each cluster serve as evidence supporting subsequent credibility assessment task. Extensive experiments on two real world datasets shows the effectiveness of the proposed approach in identifying claims.},
  keywords = {claim identification, credibility assessment, information extraction},
  url = {http://ceur-ws.org/Vol-1691/paper_01.pdf},
}

@InProceedings{microposts2016_kurka.ea:2016,
  author = {David Burth Kurka and Alan Godoy and Fernando J. {Von Zuben}},
  title = {Birds of a Feather Tweet Together: Computational Techniques to Understand User Communities in Social Networks},
  crossref = {proc_microposts2016@www2016},
  pages = {21--27},
  booktitle = {6th Workshop on Making Sense of Microposts {(\#Microposts2016)}},
  year = 2016,
  abstract = {The study of social systems shows that there is a relationship of mutual influence between social connections and individual behavior, known as homophily. In this work, we developed a methodology to allow the analysis of interests of groups of users in Twitter network, based on automatic community detection and tweets ranking. The techniques presented reveal evidences that the presence of communities is related to topic specialization, and allow the characterization of elaborate profiles of groups of users based only on their location on the network.},
  keywords = {online social networks, social network analysis, community detection, homophily, complex systems},
  url = {http://ceur-ws.org/Vol-1691/paper_05.pdf},
}


@InProceedings{microposts2016_socsci_weller.ea:2016,
  author = {Weller, Katrin and Dadzie, Aba-Sah and Radovanovi\'c, Danica},
  title = {Making Sense of Microposts ({\#Microposts2016}) Social Sciences Track},
  crossref = {proc_microposts2016@www2016},
  pages = {29--32},
  booktitle = {6th Workshop on Making Sense of Microposts {(\#Microposts2016)}},
  year = 2016,
  abstract = {For the second time, the \#Microposts workshop features a track to highlight social science perspectives on micro communication structures in online environments. This paper introduces the \emph{\#Microposts2016 (Computational) Social Science Track}, which all contribute to connecting research methods and approaches in computer science and social science. By providing a forum for closer interaction between the two fields, the track is becoming a platform for interdisciplinary projects and new ideas to combine different methodologies and theories. For this year's special track we see the trend of relating Microposts to external demographics or survey data as a way to better understand Microposts in their broader contexts.},
  keywords = {Microposts, Social Science, Web Science, Computational Social Science, Internet science, social media, user-generated content, online communication, Internet research},
  url = {http://ceur-ws.org/Vol-1691/microposts2016_socsci-report.pdf},
}

@InProceedings{microposts2016_socsci_buntain.ea:2016,
  author = {Cody Buntain and Erin McGrath and Jennifer Golbeck and Gary {LaFree}},
  title = {Comparing Social Media and Traditional Surveys around the {B}oston {M}arathon Bombing},
  crossref = {proc_microposts2016@www2016},
  pages = {34--41},
  booktitle = {6th Workshop on Making Sense of Microposts {(\#Microposts2016)}},
  year = 2016,
  abstract = {Sociological surveys have been a key instrument in understanding social phenomena, but do the introduction and popularity of social media threaten to usurp the survey's place?
The significant amount of data one can capture from social media sites like Twitter make such sources appealing.
Limited work has tried to triangulate these sources pragmatically for research.
This paper documents experiences in comparing analyses and results from a panel survey, a survey embedded within an experiment, and social media data surrounding the 2013 Boston Marathon Bombing.
Our experience suggests the sources are complementary: social media provides better insight into behavior more rapidly and cheaper than surveys, but surveys can provide higher quality, targeted, and more relevant data.},
  keywords = {social media, twitter, surveys, big data, boston marathon bombing},
  url = {http://ceur-ws.org/Vol-1691/paper_02.pdf},
}

@InProceedings{microposts2016_socsci_stier.ea:2016,
  author = {Sebastian Stier},
  title = {Studying the Role of Elites in {U.S.} Political {T}witter Debates},
  crossref = {proc_microposts2016@www2016},
  pages = {43--45},
  booktitle = {6th Workshop on Making Sense of Microposts {(\#Microposts2016)}},
  year = 2016,
  abstract = {Because of their ever-growing importance, elite actors from the political sphere and news media have integrated social network sites and especially Twitter into their communication strategies. However, the extent of these adaptation processes is not yet fully understood. This article presents lists of U.S. actors from politics, news media and government. As an exploratory analysis, the influence of elites in U.S. political Twitter debates is investigated by applying basic measures of Twitter influence to two test datasets.},
  keywords = {online political communication, Twitter, politics, news media, government},
  url = {http://ceur-ws.org/Vol-1691/paper_08.pdf},
}

@InProceedings{microposts2016_socsci_oh.ea:2016,
  author = {Alex Jeongwoo Oh and Pramuan Bunkanwanicha},
  title = {{CEO}s on {T}witter},
  crossref = {proc_microposts2016@www2016},
  pages = {46--48},
  booktitle = {6th Workshop on Making Sense of Microposts {(\#Microposts2016)}},
  year = 2016,
  abstract = {We observe many CEOs of big companies as easily as movie stars or sports players on Twitter. Why do they appear on social media and what would be the effect of their Twitter network on the corporate performance and shareholder benefit? This study explores big company CEOs on Twitter. We identify Twitter-using CEOs and examine the impact of their social media activity on the corporate performance. While academic literatures have studied the performance of publicly well-known CEOs mainly with media coverage, we focus more on the big company CEOs on the social media and test its effect on the corporate performance. We discovered the determinants of Twitter status of CEOs, in terms of personal attributes, company attributes and industry. We also found the positive impact of Twitter on corporate performance, contrary to previous evidences of negative effect.},
  keywords = {social media, corporate performance, compensation},
  url = {http://ceur-ws.org/Vol-1691/paper_04.pdf},
}


@InProceedings{microposts2016_neel_rizzo.ea:2016,
  author = {Rizzo, Giuseppe and van Erp, Marieke and Plu, Julien and Troncy, Rapha\"el},
  title = {{M}aking {S}ense of {M}icroposts (\#{M}icroposts2016) {N}amed {E}ntity {rEcognition} and {L}inking ({NEEL}) {C}hallenge},
  crossref = {proc_microposts2016@www2016},
  pages = {50--59},
  booktitle = {6th Workshop on Making Sense of Microposts {(\#Microposts2016)}},
  year = 2016,
  abstract = {This paper describes the (\#{M}icroposts2016) {\bf Named Entity rEcognition and Linking (NEEL)} Challenge, held as a track of the \textit{Making Sense of Microposts} Workshop co-located with the \textit{World Wide Web} conference (WWW'2016). The challenge task comprised automatic linking and classification of entities appearing in different event streams of English tweets. Participants were invited to develop novel strategies for extracting entities in a tweet stream, typing them based on a set of pre-defined classes, and linking them to resources from the DBpedia 2015-04 knowledge base or NIL referents. The challenge attracted a lot of interest: 37 research teams expressed an intent to participate and signed the agreement to acquire the dataset. Six teams with different approaches took part in the final evaluation of the challenge task. The submissions covered joint linguistic and graph-based entity recognition and linking methods and sequential linguistic pipelines, where the two stages are separated, for addressing the challenge task. We describe the evaluation process and discuss the performance of the different approaches that have entered the 2016 NEEL Challenge. We also release, with this paper, the corpus consisting of manually annotated tweets.},
  keywords = {Microposts, Named Entity Recognition, Named Entity Linking, Disambiguation, Knowledge Base, Evaluation, Challenge},
  url = {http://ceur-ws.org/Vol-1691/microposts2016_neel-challenge-report/},
}

@InProceedings{microposts2016_neel_waiteloni.ea:2016,
  author = {J\"org Waitelonis and Harald Sack},
  title = {Named Entity Linking in {\#Tweets} with {KEA}},
  crossref = {proc_microposts2016@www2016},
  pages = {61--63},
  booktitle = {6th Workshop on Making Sense of Microposts {(\#Microposts2016)}},
  year = 2016,
  abstract = {This paper presents the KEA system at the \#Microposts 2016 NEEL Challenge. 
Its task is to recognize and type mentions from English microposts and link them to their corresponding entries in DBpedia. 
For this task, we have adapted our Named Entity Disambiguation tool originally designed for natural language text to the special requirements of noisy, terse, and poorly worded tweets containing special functional terms and language.},
  keywords = {named entity linking, disambiguation, Microposts},
  url = {http://ceur-ws.org/Vol-1691/paper_14.pdf},
}

@InProceedings{microposts2016_neel_torresTramon.ea:2016,
  author = {Pablo Torres-Tram\'on and Hugo Hromic and Brian Walsh and Bahareh R. Heravi and Conor Hayes},
  title = {{Kanopy4Tweets}: Entity Extraction and Linking for {T}witter},
  crossref = {proc_microposts2016@www2016},
  pages = {64--66},
  booktitle = {6th Workshop on Making Sense of Microposts {(\#Microposts2016)}},
  year = 2016,
  abstract = {Named Entity rEcognition and Linking (NEEL) from text is an essential task in many Natural Language Processing (NLP) applications because it enables a better understanding of the content. However in the context of Social Media, NEEL is challenging due to the higher level of writing mistakes, fast language dynamics and often lack of context. To this end, we adapted \textsc{Kanopy} -- an unsupervised graph-based topic disambiguation system -- to be used for the task of NEEL in the domain of Twitter, a fast-paced micro-blogging platform. We describe the design of our solution and report the results obtained by our system using the official corpus of Tweets for the NEEL 2016 Challenge},
  keywords = {information extraction, Twitter, entity linking},
  url = {http://ceur-ws.org/Vol-1691/paper_13.pdf},
}

@InProceedings{microposts2016_neel_greenfield.ea:2016,
  author = {Kara Greenfield and Rajmonda Caceres and Michael Coury and Kelly Geyer and Youngjune Gwon and Jason Matterer and Alyssa Mensch and Cem Sahin and Olga Simek},
  title = {A Reverse Approach to Named Entity Extraction and Linking in Microposts},
  crossref = {proc_microposts2016@www2016},
  pages = {67--69},
  booktitle = {6th Workshop on Making Sense of Microposts {(\#Microposts2016)}},
  year = 2016,
  abstract = {In this paper, we present a pipeline for named entity extraction and linking that is designed specifically for noisy, grammatically inconsistent domains where traditional named entity techniques perform poorly. Our approach leverages a large knowledge base to improve entity recognition, while maintaining the use of traditional NER to identify mentions that are not co-referent with any entities in the knowledge base.},
  keywords = {named entity recognition, entity linking, Twitter DBpedia, social media},
  url = {http://ceur-ws.org/Vol-1691/paper_11.pdf},
}

@InProceedings{microposts2016_neel_caliano.ea:2016,
  author = {Davide Caliano and Elisabetta Fersini and Pikakshi Manchanda and Matteo Palmonari and Enza Messina},
  title = {{UniMiB}: Entity Linking in Tweets using {Jaro-Winkler} Distance, Popularity and Coherence},
  crossref = {proc_microposts2016@www2016},
  pages = {70--72},
  booktitle = {6th Workshop on Making Sense of Microposts {(\#Microposts2016)}},
  year = 2016,
  abstract = {This paper summarizes the participation of UNIMIB team in the Named Entity rEcognition and Linking (NEEL) Challenge in \#Microposts2016. In this paper, we propose a knowledge-base approach for identifying and linking named entities from tweets. The named entities are, further, classified using evidence provided by our entity linking algorithm and type-casted into Microposts categories.},
  keywords = {knowledge base, named entity recognition, named entity linking},
  url = {http://ceur-ws.org/Vol-1691/paper_12.pdf},
}

@InProceedings{microposts2016_neel_ghosh.ea:2016,
  author = {Souvick Ghosh and Promita Maitra and Dipankar Das},
  title = {Feature Based Approach to Named Entity Recognition and Linking for Tweets},
  crossref = {proc_microposts2016@www2016},
  pages = {74--76},
  booktitle = {6th Workshop on Making Sense of Microposts {(\#Microposts2016)}},
  year = 2016,
  abstract = {In this paper, we describe our approach for Named Entity rEcognition and Linking Challenge (NEEL) at the \#Microposts2016.  The task is to automatically recognize entities and their types from English microposts, and link them to corresponding DBpedia 2015 entries. If the resources do not exist, we use NIL identifiers instead. The task is unique as twitter data is informal in nature with non-conformational spellings, random contractions and various other noises. For this task, we developed our system using a hybrid model. We have used various existing named entity recognition (NER) systems and combined them with our classifier to improve the results.},
  keywords = {named entity extraction; named entity linking, social media, dbpedia, Twitter},
  url = {http://ceur-ws.org/Vol-1691/paper_10.pdf},
}


@InProceedings{microposts2016_lbw_geyer.ea:2016,
  author = {Kelly Geyer and Kara Greenfield and Alyssa Mensch and Olga Simek},
  title = {Named Entity Recognition in 140 Characters or Less},
  crossref = {proc_microposts2016@www2016},
  pages = {78--79},
  booktitle = {6th Workshop on Making Sense of Microposts {(\#Microposts2016)}},
  year = 2016,
  abstract = {In this paper, we explore the problem of recognizing named entities in microposts, a genre with notoriously little context surrounding each named entity and inconsistent use of grammar, punctuation, capitalization, and spelling conventions by authors. In spite of the challenges associated with information extraction from microposts, it remains an increasingly important genre. This paper presents the MIT Information Extraction Toolkit (MITIE) and explores its adaptability to the micropost genre.},
  keywords = {named entity recognition, re-training, social media, Twitter},
  url = {http://ceur-ws.org/Vol-1691/paper_16.pdf},
}
