Index of papers in Proc. ACL that mention
  • social media
Wang, Jia and Li, Qing and Chen, Yuanzhu Peter and Lin, Zhangxi
Abstract
The variety of engaging interactions among users in social medial distinguishes it from traditional Web media.
Abstract
Such a feature should be utilized while attempting to provide intelligent services to social media participants.
Abstract
One of the most important observation from this work is that semantic contents of user comments can play a fairly different role in a different form of social media .
Experimental Evaluation
This simulates the scenario of recommending relevant news from traditional media to social media users for their further reading.
Experimental Evaluation
In this part, we explore the contribution of user authority and comments in social media recommender.
Introduction
In a more general context, Web is one of the most important carriers for “social media” , e. g. In-
Introduction
Various engaging interactions among users in social media differentiate it from traditional Web sites.
Introduction
Such characteristics should be utilized in attempt to provide intelligent services to social media users.
Related Work
Most recent researches on information recommendation in social media focus on the blogosphere.
Related Work
different social media corpora (Section 4).
social media is mentioned in 15 sentences in this paper.
Topics mentioned in this paper:
Wang, Baoxun and Wang, Xiaolong and Sun, Chengjie and Liu, Bingquan and Sun, Lin
Abstract
Quantifying the semantic relevance between questions and their candidate answers is essential to answer detection in social media corpora.
Introduction
Obviously, these natural QA pairs are usually created during people’s communication via Internet social media , among which we are interested in the community-driven
Introduction
In this paper, a novel approach for modeling the semantic relevance for QA pairs in the social media sites is proposed.
Introduction
As mentioned above, the user generated questions and their answers via social media are always short texts.
Learning with Homogenous Data
Our motivation of finding the homogenous question-answer corpora from different kind of social media is to guarantee the model’s performance and avoid hand-annotating work.
Learning with Homogenous Data
It indicates that the word distributions of the two corpora are quite similar, although they come from different social media sites.
Learning with Homogenous Data
The task of detecting answers in social media corpora suffers from the problem of feature sparsity seriously.
Related Work
(2009) both propose the strategies to detect questions in the social media corpus, which is proved to be a nontrivial task.
The Deep Belief Network for QA pairs
Due to the feature sparsity and the low word frequency of the social media corpus, it is difficult to model the semantic relevance between questions and answers using only co-occurrence features.
The Deep Belief Network for QA pairs
In the social media corpora, the answers are always descriptive, containing one or several sentences.
social media is mentioned in 11 sentences in this paper.
Topics mentioned in this paper:
Rosenthal, Sara and McKeown, Kathleen
Abstract
Through experimentation with a range of years, we found that the birth dates of students in college at the time when social media such as AIM, SMS text messaging, MySpace and Facebook first became popular, enable accurate age prediction.
Experiments and Results
5.2 Social Media and Generation Y
Experiments and Results
We were motivated to examine these years due to the emergence of social media technologies during that time.
Experiments and Results
Generation Y is considered the social media generation, so we decided to examine how the creation and/ or popularity of social media technologies compared to the years that had a change in writing style.
Introduction
The users of these social media platforms have created their own form of unstructured writing that is best characterized as informal.
Introduction
social media generation.
Introduction
We focus on this generation due to the rise of popular social media technologies such as messaging and online social networks sites that occurred during that time.
Related Work
Their work shows that ease of classification is dependent in part on what division is made between age groups and in turn motivates our decision to study whether the creation of social media technologies can be used to find the dividing line(s).
social media is mentioned in 15 sentences in this paper.
Topics mentioned in this paper:
Huang, Hongzhao and Wen, Zhen and Yu, Dian and Ji, Heng and Sun, Yizhou and Han, Jiawei and Li, He
Experiments
As shown in Table9 (K is the number of predefined topics), PLSA is not quite effective mainly because traditional topic modeling approaches do not perform well on short texts from social media .
Introduction
The proliferation of online social media significantly expedites this evolution, as new phrases triggered by social events may be disseminated rapidly in social media .
Introduction
To automatically analyze such fast evolving language in social media , new computational models are demanded.
Introduction
We believe that successful resolution of morphs is a crucial step for automated understanding of the fast evolving social media language, which is important for social media marketing (Bar-wise and Meehan, 2010).
Related Work
To analyze social media behavior under active censorship, (Bamman et al., 2012) automatically discovered politically sensitive terms from Chinese tweets based on message deletion analysis.
Target Candidate Ranking
Unfortunately the state-of-the-art techniques for these tasks still perform poorly on social media in terms of both accuracy and coverage of important information, these sophisticated semantic links all produced negative impact on the target ranking performance.
Target Candidate Ranking
In contrast, users are less restricted in some other uncensored social media such as Twitter.
Target Candidate Ranking
Because of such social correlation, close social neighbors in social media such as Twitter and Weibo may post similar information, or share similar opinion.
social media is mentioned in 11 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:
Takase, Sho and Murakami, Akiko and Enoki, Miki and Okazaki, Naoaki and Inui, Kentaro
Abstract
There are some chronic critics who always complain about the entity in social media .
Abstract
In social media , most comments are informal, and, there are sarcastic and incomplete contexts.
Abstract
As an alternative approach for social media , we can assume that users who share the same opinions will link to each other.
Introduction
On a social media website, there may be millions of users and large numbers of comments.
Introduction
The comments in social media are related to the real world in such fields as marketing and politics.
Introduction
Analyzing comments in social media has been shown to be effective in predicting the behaviors of stock markets and of voters in elections (Bollen et al., 2011; Tumasjan et al., 2010; O’Connor et al., 2010).
social media is mentioned in 16 sentences in this paper.
Topics mentioned in this paper:
Volkova, Svitlana and Wilson, Theresa and Yarowsky, David
Abstract
We study subjective language in social media and create Twitter-specific lexicons via bootstrapping sentiment-bearing terms from multilingual Twitter streams.
Abstract
Our experiments on English, Spanish and Russian show that the resulting lexicons are effective for sentiment classification for many under-explored languages in social media .
Introduction
This is true for well-formed data, such as news and reviews, and it is particularly true for data from social media .
Introduction
Communication in social media is informal, abbreviations and misspellings abound, and the person communicating is often trying to be funny, creative, and entertaining.
Introduction
The dynamic nature of social media together with the extreme diversity of subjective language has implications for any system with the goal of analyzing sentiment in this domain.
Related Work
However, the lexical resources that dictionary-based methods need, do not yet exist for the majority of languages in social media .
social media is mentioned in 14 sentences in this paper.
Topics mentioned in this paper:
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:
Bergsma, Shane and Van Durme, Benjamin
Abstract
We describe a novel approach for automatically predicting the hidden demographic properties of social media users.
Applying Class Attributes
5While we used an “off the shelf” POS tagger in this work, we note that taggers optimized specifically for social media are now available and would likely have resulted in higher tagging accuracy (e. g. Owoputi et al.
Introduction
There has been growing interest in characterizing social media users based on the content they generate; that is, automatically labeling users with demographic categories such as age and gender (Burger and Henderson, 2006; Schler et al., 2006; Rao et al., 2010; Mukherjee and Liu, 2010; Pennacchiotti and Popescu, 2011; Burger et al., 2011; Van Durme, 2012).
Introduction
t0 Characterize Social Media Users
Learning Class Attributes
A leg is a relevant and correct part of both a male and a female (and many other living and inanimate objects), but it does not help us distinguish males from females in social media .
Related Work
Many recent papers have analyzed the language of social media users, along dimensions such as ethnicity (Eisenstein et al., 2011; Rao et al., 2011; Pennacchiotti and Popescu, 2011; Fink et al., 2012) time zone (Kiciman, 2010), political orientation (Rao et al., 2010; Pennacchiotti and Popescu, 2011) and gender (Rao et al., 2010; Burger et al., 2011; Van Durme, 2012).
Results
7Note that it is possible to achieve even higher performance on gender classification in social media if you have further information about a user, such as their full first and last name (Burger et al., 2011; Bergsma et al., 2013).
Results
This is important because having thousands of gold standard annotations for every possible user characterization task, in every domain and social media platform, is not realistic.
Supervised User Characterization
Using a combination of content and username features “represents a use case common to many different social media sites, such as chat rooms and news article comment streams” (Burger et al., 2011).
Twitter Gender Prediction
We can therefore benchmark our approach against state-of-the-art supervised systems trained with plentiful gold-standard data, giving us an idea of how well our Bootstrapped system might compare to theoretically top-performing systems on other tasks, domains, and social media platforms where such gold-standard training data is not available.
social media is mentioned in 10 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:
Benson, Edward and Haghighi, Aria and Barzilay, Regina
Conclusion
We presented a novel model for record extraction from social media streams such as Twitter.
Evaluation
While our experiments utilized binary relations, we believe our general approach should be useful for nary relation recovery in the social media domain.
Introduction
We propose a method for discovering event records from social media feeds such as Twitter.
Introduction
Social media messages are often short, make heavy use of colloquial language, and require situational context for interpretation (see examples in Figure 1).
Introduction
Social Media Feeds
social media is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Lampos, Vasileios and Preoţiuc-Pietro, Daniel and Cohn, Trevor
Abstract
Social Media contain a multitude of user opinions which can be used to predict real-world phenomena in many domains including politics, finance and health.
Abstract
These techniques require very careful filtering of the input texts, as most Social Media posts are irrelevant to the task.
Data
For the evaluation of the proposed methodologies we have created two data sets of Social Media content with different characteristics based in the UK and Austria respectively.
Data
Data processing is performed using the TrendMiner architecture for Social Media analysis (Preotiuc-Pietro et al., 2012).
Introduction
Web Social Media platforms have ushered a new era in human interaction and communication.
Methods
The textual content posted on Social Media platforms unarguably contains valuable information, but quite often it is hidden under vast amounts of unstructured user generated input.
Related Work
The topic of political opinion mining from Social Media has been the focus of various recent research works.
social media is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Surdeanu, Mihai and Ciaramita, Massimiliano and Zaragoza, Hugo
Conclusions
On the other hand, we expect the outcome of this process to help several applications, such as open-domain QA on the Web and retrieval from social media .
Conclusions
On social media , our system should be combined with a component that searches for similar questions already answered; this output can possibly be filtered further by a content-quality module that explores “social” features such as the authority of users, etc.
Introduction
On the other hand, recent years have seen an explosion of user-generated content (or social media ).
Introduction
In this paper we address the problem of answer ranking for non-factoid questions from social media content.
Related Work
In fact, it is likely that an optimal retrieval engine from social media should combine all these three methodologies.
Related Work
Moreover, our approach might have applications outside of social media (e.g., for open-domain web-based QA), because the ranking model built is based only on open-domain knowledge and the analysis of textual content.
social media is mentioned in 6 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:
Tomasoni, Mattia and Huang, Minlie
Discussion and Future Directions
The Quality assessing component itself could be built as a module that can be adjusted to the kind of Social Media in use; the creation of customized Quality feature spaces would make it possible to handle different sources of UGC (forums, collaborative authoring websites such as Wikipedia, blogs etc.).
Introduction
Community Question Answering (cQA) portals are an example of Social Media where the information need of a user is expressed in the form of a question for which a best answer is picked among the ones generated by other users.
Introduction
Interestingly, a great amount of information is embedded in the metadata generated as a byproduct of users’ action and interaction on Social Media .
The summarization framework
Quality assessing of information available on Social Media had been studied before mainly as a binary classification problem with the objective of detecting low quality content.
social media is mentioned in 4 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:
Si, Jianfeng and Mukherjee, Arjun and Liu, Bing and Li, Qing and Li, Huayi and Deng, Xiaotie
Introduction
Social media websites such as Twitter, Facebook, etc., have become ubiquitous platforms for social networking and content sharing.
Related Work 2.1 Market Prediction and Social Media
Some recent researches suggest that news and social media such as blogs, micro-blogs, etc., can be analyzed to extract public sentiments to help predict the market (La-vrenko et al., 2000; Schumaker and Chen, 2009).
Related Work 2.1 Market Prediction and Social Media
The topics mostly focus on hot keywords like: news, stocknews, earning, report, which stimulate active discussions on the social media platform.
social media is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Qazvinian, Vahed and Radev, Dragomir R.
Introduction
In this paper, we focus on the computational analysis of collective discourse, a collective behavior seen in interactive content contribution and text summarization in online social media .
Introduction
In social media , discourse (Grosz and Sidner, 1986) is Often a collective reaction to an event.
Prior Work
Finally, recent research on analyzing online social media shown a growing interest in mining news stories and headlines because of its broad applications ranging from “meme” tracking and spike detection (Leskovec et al., 2009) to text summarization (Barzilay and McKeown, 2005).
social media is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: