Index of papers in Proc. ACL 2014 that mention
  • social media
Li, Jiwei and Ritter, Alan and Hovy, Eduard
Abstract
While user attribute extraction on social media has received considerable attention, existing approaches, mostly supervised, encounter great difficulty in obtaining gold standard data and are therefore limited to predicting unary predicates (e.g., gender).
Abstract
Users’ profiles from social media websites such as Facebook or Google Plus are used as a distant source of supervision for extraction of their attributes from user-generated text.
Abstract
In addition to traditional linguistic features used in distant supervision for information extraction, our approach also takes into account network information, a unique opportunity offered by social media .
Dataset Creation
Spouse Facebook is the only type of social media where spouse information is commonly displayed.
Introduction
The overwhelming popularity of online social media creates an opportunity to display given aspects of oneself.
Introduction
We are optimistic that our approach can easily be applied to further user attributes such as HOBBIES and INTERESTS (MOVIES, BOOKS, SPORTS or STARS), RELIGION, HOMETOWN, LIVING LOCATION, FAMILY MEMBERS and so on, where training data can be obtained by matching ground truth retrieved from multiple types of online social media such as Facebook, Google Plus, or LinkedIn.
Introduction
0 We present a large-scale dataset for this task gathered from various structured and unstructured social media sources.
Related Work
While user profile inference from social media has received considerable attention (Al Zamal et al., 2012; Rao and Yarowsky, 2010; Rao et al., 2010; Rao et al., 2011), most previous work has treated this as a classification task where the goal is to predict unary predicates describing attributes of the user.
Related Work
Homophily Online social media offers a rich source of network information.
Related Work
(2001) discovered that people sharing more attributes such as background or hobby have a higher chance of becoming friends in social media .
social media is mentioned in 12 sentences in this paper.
Topics mentioned in this paper:
Peng, Nanyun and Wang, Yiming and Dredze, Mark
Abstract
Code-switched documents are common in social media , providing evidence for polylingual topic models to infer aligned topics across languages.
Code-Switching
Code-switching specifically in social media has also received some recent attention.
Introduction
Topic models (Blei et al., 2003) have become standard tools for analyzing document collections, and topic analyses are quite common for social media (Paul and Dredze, 2011; Zhao et al., 2011; Hong and Davison, 2010; Ramage et al., 2010; Eisenstein et al., 2010).
Introduction
In social media especially, there is a large diversity in terms of both the topic and language, necessitating the modeling of multiple languages simultaneously.
Introduction
However, the ever changing vocabulary and topics of social media (Eisenstein, 2013) make finding suitable comparable corpora difficult.
social media is mentioned in 16 sentences in this paper.
Topics mentioned in this paper:
Volkova, Svitlana and Coppersmith, Glen and Van Durme, Benjamin
Abstract
Existing models for social media personal analytics assume access to thousands of messages per user, even though most users author content only sporadically over time.
Batch Models
The proposed baseline model follows the same trends as the existing state-of-the-art approaches for user attribute classification in social media as described in Section 8.
Batch Models
7We use log-linear models over reasonable alternatives such as perceptron or SVM, following the practice of a wide range of previous work in related areas (Smith, 2004; Liu et a1., 2005; Poon et a1., 2009) including text classification in social media (Van Durme, 2012b; Yang and Eisenstein, 2013).
Batch Models
Following the streaming nature of social media , we see the scarce available resource as the number of requests allowed per day to the Twitter API.
Conclusions and Future Work
This may be also the effect of data heterogeneity in social media compared to e.g., political debate text (Thomas et al., 2006).
Introduction
Inferring latent user attributes such as gender, age, and political preferences (Rao et al., 2011; Zamal et al., 2012; Cohen and Ruths, 2013) automatically from personal communications and social media including emails, blog posts or public discussions has become increasingly popular with the web getting more social and volume of data available.
Introduction
In this paper we analyze and go beyond static models formulating personal analytics in social media as a streaming task.
Related Work
Supervised Batch Approaches The vast majority of work on predicting latent user attributes in social media apply supervised static SVM models for discrete categorical e.g., gender and regression models for continuous attributes e.g., age with lexical bag-of-word features for classifying user gender (Garera and Yarowsky, 2009; Rao et al., 2010; Burger et al., 2011; Van Durme, 2012b), age (Rao et al., 2010; Nguyen et al., 2011; Nguyen et al., 2013) or political orientation.
Related Work
Additionally, using social media for mining political opinions (O’Connor et al., 2010a; Maynard and Funk, 2012) or understanding sociopolitical trends and voting outcomes (Tumasjan et al., 2010; Gayo-Avello, 2012; Lampos et al., 2013) is becoming a common practice.
Related Work
(2013) propose a bilinear user-centric model for predicting voting intentions in the UK and Australia from social media data.
social media is mentioned in 13 sentences in this paper.
Topics mentioned in this paper:
Zhou, Deyu and Chen, Liangyu and He, Yulan
Abstract
With the proliferation of social media sites, social streams have proven to contain the most up-to-date information on current events.
Abstract
However, it is not straightforward to adapt the existing event extraction systems since texts in social media are fragmented and noisy.
Abstract
In this paper we propose a simple and yet effective Bayesian model, called Latent Event Model (LEM), to extract structured representation of events from social media .
Conclusions and Future Work
In this paper we have proposed an unsupervised Bayesian model, called the Latent Event Model (LEM), to extract the structured representation of events from social media data.
Introduction
With the increasing popularity of social media , social networking sites such as Twitter have become an important source of event information.
Introduction
Social media messages are often short and evolve rapidly over time.
Introduction
In our work here, we notice a very important property in social media data that the same event could be referenced by high volume messages.
social media is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Soni, Sandeep and Mitra, Tanushree and Gilbert, Eric and Eisenstein, Jacob
Introduction
Contemporary journalism is increasingly conducted through social media services like Twitter (Lotan et al., 2011; Hermida et al., 2012).
Introduction
However, less is known about this phenomenon in social media — a domain whose endemic uncertainty makes proper treatment of factuality even more crucial (Morris et al., 2012).
Related work
search, which focuses on quoted statements in social media text.
Related work
Credibility in social media Recent work in the area of computational social science focuses on understanding credibility cues on Twitter.
Related work
The search for reliable signals of information credibility in social media has led to the construction of automatic classifiers to identify credible tweets (Castillo et al., 2011).
social media is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Plank, Barbara and Hovy, Dirk and Sogaard, Anders
Annotator disagreements across domains and languages
Besides these English data sets, we also obtained doubly-annotated POS data from the French Social Media Bank project (Seddah et al., 2012).3 All data sets, except the French one, are publicly available at http: / /lowlands .
Annotator disagreements across domains and languages
Lastly, we compare the disagreements of annotators on a French social media data set (Seddah et al., 2012), which we mapped to the universal POS tag set.
Hard cases and annotation errors
Figure 3: Disagreement on French social media
Introduction
N OUN VERB ADP/PRT ADV/NOUN (2) Noam likes social media
social media is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Salway, Andrew and Touileb, Samia
Approach
To address the large scale and complexity of language use in social media , we modify the way in which text is presented to ADIOS by focusing separately on text around key terms of interest, rather than processing all sentences en masse.
Introduction
There is an obvious need for text mining techniques to deal with large volumes of very diverse material, especially since the advent of social media and user-generated content which includes dynamic discussions of wide-ranging and controversial topics.
Introduction
We see one particular area of application in elucidating the semantic content of social media debates about controversial topics, like climate change, both for casual users, and for social scientists studying online discourses.
social media is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Severyn, Aliaksei and Moschitti, Alessandro and Uryupina, Olga and Plank, Barbara and Filippova, Katja
Introduction
Social media such as Twitter, Facebook or YouTube contain rapidly changing information generated by millions of users that can dramatically affect the reputation of a person or an organization.
Introduction
This raises the importance of automatic extraction of sentiments and opinions expressed in social media .
Related work
The aforementioned corpora are, however, only partially suitable for developing models on social media , since the informal text poses additional challenges for Information Extraction and Natural Language Processing.
social media is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: