prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. Do they use large or small BERT?
Context 1: Introduction
Word Sense Disambiguation (WSD) is a fundamental task and long-standing challenge in Natural Language Processing (NLP), which aims to find the exact sense of an ambiguous word in a particular context BIBREF0  Previous WSD approaches can be grouped into two main categories: knowledge-based and supervised methods 
Knowledge-based WSD methods rely on lexical resources like WordNet BIBREF1 and usually exploit two kinds of lexical knowledge  The gloss, which defines a word sense meaning, is first utilized in Lesk algorithm BIBREF2 and then widely taken into account in many other approaches BIBREF3, BIBREF4  Besides, structural properties of semantic graphs are mainly used in graph-based algorithms BIBREF5, BIBREF6 
Traditional supervised WSD methods BIBREF7, BIBREF8, BIBREF9 focus on extracting manually designed features and then train a dedicated classifier (word expert) for every target lemma 
Although word expert supervised WSD methods perform better, they are less flexible than knowledge-based methods in the all-words WSD task BIBREF10  Recent neural-based methods are devoted to dealing with this problem  BIBREF11 present a supervised classifier based on Bi-LSTM, which shares parameters among all word types except the last layer  BIBREF10 convert WSD task to a sequence labeling task, thus building a unified model for all polysemous words  However, neither of them can totally beat the best word expert supervised methods 
More recently, BIBREF12 propose to leverage the gloss information from WordNet and model the semantic relationship between the context and gloss in an improved memory network  Similarly, BIBREF13 introduce a (hierarchical) co-attention mechanism to generate co-dependent representations for the context and gloss  Their attempts prove that incorporating gloss knowledge into supervised WSD approach is helpful, but they still have not achieved much improvement, because they may not make full use of gloss knowledge 
In this paper, we focus on how to better leverage gloss information in a supervised neural WSD system  Recently, the pre-trained language models, such as ELMo BIBREF14 and BERT BIBREF15, have shown their effectiveness to alleviate the effort of feature engineering  Especially, BERT has achieved excellent results in question answering (QA) and natural language inference (NLI)  We construct context-gloss pairs from glosses of all possible senses (in WordNet) of the target word, thus treating WSD task as a sentence-pair classification problem  We fine-tune the pre-trained BERT model and achieve new state-of-the-art results on WSD task  In particular, our contribution is two-fold:
1  We construct context-gloss pairs and propose three BERT-based models for WSD 
2  We fine-tune the pre-trained BERT model, and the experimental results on several English all-words WSD benchmark datasets show that our approach significantly outperforms the state-of-the-art systems 
Methodology
In this section, we describe our method in detail 
Methodology ::: Task Definition
In WSD, a sentence $s$ usually consists of a series of words: $\lbrace w_1,\cdots ,w_m\rbrace $, and some of the words $\lbrace w_{i_1},\cdots ,w_{i_k}\rbrace $ are targets $\lbrace t_1,\cdots ,t_k\rbrace $ need to be disambiguated  For each target $t$, its candidate senses $\lbrace c_1,\cdots ,c_n\rbrace $ come from entries of its lemma in a pre-defined sense inventory (usually WordNet)  Therefore, WSD task aims to find the most suitable entry (symbolized as unique sense key) for each target in a sentence  See a sentence example in Table TABREF1 
Methodology ::: BERT
BERT BIBREF15 is a new language representation model, and its architecture is a multi-layer bidirectional Transformer encoder  BERT model is pre-trained on a large corpus and two novel unsupervised prediction tasks, i e , masked language model and next sentence prediction tasks are used in pre-training  When incorporating BERT into downstream tasks, the fine-tuning procedure is recommended  We fine-tune the pre-trained BERT model on WSD task 
Methodology ::: BERT ::: BERT(Token-CLS)
Since every target in a sentence needs to be disambiguated to find its exact sense, WSD task can be regarded as a token-level classification task  To incorporate BERT to WSD task, we take the final hidden state of the token corresponding to the target word (if more than one token, we average them) and add a classification layer for every target lemma, which is the same as the last layer of the Bi-LSTM model BIBREF11 
Methodology ::: GlossBERT
BERT can explicitly model the relationship of a pair of texts, which has shown to be beneficial to many pair-wise natural language understanding tasks  In order to fully leverage gloss information, we propose GlossBERT to construct context-gloss pairs from all possible senses of the target word in WordNet, thus treating WSD task as a sentence-pair classification problem 
We describe our construction method with an example (See Table TABREF1)  There are four targets in this sentence, and here we take target word research as an example:
Methodology ::: GlossBERT ::: Context-Gloss Pairs
The sentence containing target words is denoted as context sentence  For each target word, we extract glosses of all $N$ possible senses (here $N=4$) of the target word (research) in WordNet to obtain the gloss sentence  [CLS] and [SEP] marks are added to the context-gloss pairs to make it suitable for the input of BERT model  A similar idea is also used in aspect-based sentiment analysis BIBREF16 
Methodology ::: GlossBERT ::: Context-Gloss Pairs with Weak Supervision
Based on the previous construction method, we add weak supervised signals to the context-gloss pairs (see the highlighted part in Table TABREF1)  The signal in the gloss sentence aims to point out the target word, and the signal in the context sentence aims to emphasize the target word considering the situation that a target word may occur more than one time in the same sentence 
Therefore, each target word has $N$ context-gloss pair training instances ($label\in \lbrace yes, no\rbrace $)  When testing, we output the probability of $label=yes$ of each context-gloss pair and choose the sense corresponding to the highest probability as the prediction label of the target word  We experiment with three GlossBERT models:
Methodology ::: GlossBERT ::: GlossBERT(Token-CLS)
We use context-gloss pairs as input  We highlight the target word by taking the final hidden state of the token corresponding to the target word (if more than one token, we average them) and add a classification layer ($label\in \lbrace yes, no\rbrace $) 
Methodology ::: GlossBERT ::: GlossBERT(Sent-CLS)
We use context-gloss pairs as input  We take the final hidden state of the first token [CLS] as the representation of the whole sequence and add a classification layer ($label\in \lbrace yes, no\rbrace $), which does not highlight the target word 
Methodology ::: GlossBERT ::: GlossBERT(Sent-CLS-WS)
We use context-gloss pairs with weak supervision as input  We take the final hidden state of the first token [CLS] and add a classification layer ($label\in \lbrace yes, no\rbrace $), which weekly highlight the target word by the weak supervision 
Experiments ::: Datasets
The statistics of the WSD datasets are shown in Table TABREF12 
Experiments ::: Datasets ::: Training Dataset
Following previous work BIBREF13, BIBREF12, BIBREF10, BIBREF17, BIBREF9, BIBREF7, we choose SemCor3 0 as training corpus, which is the largest corpus manually annotated with WordNet sense for WSD 
Experiments ::: Datasets ::: Evaluation Datasets
We evaluate our method on several English all-words WSD datasets  For a fair comparison, we use the benchmark datasets proposed by BIBREF17 which include five standard all-words fine-grained WSD datasets from the Senseval and SemEval competitions: Senseval-2 (SE2), Senseval-3 (SE3), SemEval-2007 (SE07), SemEval-2013 (SE13) and SemEval-2015 (SE15)  Following BIBREF13, BIBREF12 and BIBREF10, we choose SE07, the smallest among these test sets, as the development set 
Experiments ::: Datasets ::: WordNet
Since BIBREF17 map all the sense annotations in these datasets from their original versions to WordNet 3 0, we extract word sense glosses from WordNet 3 0 
Experiments ::: Settings
We use the pre-trained uncased BERT$_\mathrm {BASE}$ model for fine-tuning, because we find that BERT$_\mathrm {LARGE}$ model performs slightly worse than BERT$_\mathrm {BASE}$ in this task  The number of Transformer blocks is 12, the number of the hidden layer is 768, the number of self-attention heads is 12, and the total number of parameters of the pre-trained model is 110M  When fine-tuning, we use the development set (SE07) to find the optimal settings for our experiments  We keep the dropout probability at 0 1, set the number of epochs to 4  The initial learning rate is 2e-5, and the batch size is 64 
Experiments ::: Results
Table TABREF19 shows the performance of our method on the English all-words WSD benchmark datasets  We compare our approach with previous methods 
The first block shows the MFS baseline, which selects the most frequent sense in the training corpus for each target word 
The second block shows two knowledge-based systems  Lesk$_{ext+emb}$ BIBREF4 is a variant of Lesk algorithm BIBREF2 by calculating the gloss-context overlap of the target word  Babelfy BIBREF6 is a unified graph-based approach which exploits the semantic network structure from BabelNet 
The third block shows two word expert traditional supervised systems  IMS BIBREF7 is a flexible framework which trains SVM classifiers and uses local features  And IMS$_{+emb}$ BIBREF9 is the best configuration of the IMS framework, which also integrates word embeddings as features 
The fourth block shows several recent neural-based methods  Bi-LSTM BIBREF11 is a baseline for neural models  Bi-LSTM$_{+ att  + LEX + POS}$ BIBREF10 is a multi-task learning framework for WSD, POS tagging, and LEX with self-attention mechanism, which converts WSD to a sequence learning task  GAS$_{ext}$ BIBREF12 is a variant of GAS which is a gloss-augmented variant of the memory network by extending gloss knowledge  CAN$^s$ and HCAN BIBREF13 are sentence-level and hierarchical co-attention neural network models which leverage gloss knowledge 
In the last block, we report the performance of our method  BERT(Token-CLS) is our baseline, which does not incorporate gloss information, and it performs slightly worse than previous traditional supervised methods and recent neural-based methods  It proves that directly using BERT cannot obtain performance growth  The other three methods outperform other models by a substantial margin, which proves that the improvements come from leveraging BERT to better exploit gloss information  It is worth noting that our method achieves significant improvements in SE07 and Verb over previous methods, which have the highest ambiguity level among all datasets and all POS tags respectively according to BIBREF17 
Moreover, GlossBERT(Token-CLS) performs better than GlossBERT(Sent-CLS), which proves that highlighting the target word in the sentence is important  However, the weakly highlighting method GlossBERT(Sent-CLS-WS) performs best in most circumstances, which may result from its combination of the advantages of the other two methods 
Experiments ::: Discussion
There are two main reasons for the great improvements of our experimental results  First, we construct context-gloss pairs and convert WSD problem to a sentence-pair classification task which is similar to NLI tasks and train only one classifier, which is equivalent to expanding the corpus  Second, we leverage BERT BIBREF15 to better exploit the gloss information  BERT model shows its advantage in dealing with sentence-pair classification tasks by its amazing improvement on QA and NLI tasks  This advantage comes from both of its two novel unsupervised prediction tasks 
Compared with traditional word expert supervised methods, our GlossBERT shows its effectiveness to alleviate the effort of feature engineering and does not require training a dedicated classifier for every target lemma  Up to now, it can be said that the neural network method can totally beat the traditional word expert method  Compared with recent neural-based methods, our solution is more intuitive and can make better use of gloss knowledge  Besides, our approach demonstrates that when we fine-tune BERT on a downstream task, converting it into a sentence-pair classification task may be a good choice 
Conclusion
In this paper, we seek to better leverage gloss knowledge in a supervised neural WSD system  We propose a new solution to WSD by constructing context-gloss pairs and then converting WSD to a sentence-pair classification task  We fine-tune the pre-trained BERT model and achieve new state-of-the-art results on WSD task 
Acknowledgments
We would like to thank the anonymous reviewers for their valuable comments  The research work is supported by National Natural Science Foundation of China (No  61751201 and 61672162), Shanghai Municipal Science and Technology Commission (16JC1420401 and 17JC1404100), Shanghai Municipal Science and Technology Major Project (No 2018SHZDZX01) and ZJLab 
Context 3: Introduction
Over the past two decades, the emergence of social media has enabled the proliferation of traceable human behavior  The content posted by users can reflect who their friends are, what topics they are interested in, or which company they are working for  At the same time, users are listing a number of profile fields to define themselves to others  The utilization of such metadata has proven important in facilitating further developments of applications in advertising BIBREF0 , personalization BIBREF1 , and recommender systems BIBREF2   However, profile information can be limited, depending on the platform, or it is often deliberately omitted BIBREF3   To uncloak this information, a number of studies have utilized social media users' footprints to approximate their profiles 
This paper explores the potential of predicting a user's industry –the aggregate of enterprises in a particular field– by identifying industry indicative text in social media  The accurate prediction of users' industry can have a big impact on targeted advertising by minimizing wasted advertising BIBREF4 and improved personalized user experience  A number of studies in the social sciences have associated language use with social factors such as occupation, social class, education, and income BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8   An additional goal of this paper is to examine such findings, and in particular the link between language and occupational class, through a data-driven approach 
In addition, we explore how meaning changes depending on the occupational context  By leveraging word embeddings, we seek to quantify how, for example, cloud might mean a separate concept (e g , condensed water vapor) in the text written by users that work in environmental jobs while it might be used differently by users in technology occupations (e g , Internet-based computing) 
Specifically, this paper makes four main contributions  First, we build a large, industry-annotated dataset that contains over 20,000 blog users  In addition to their posted text, we also link a number of user metadata including their gender, location, occupation, introduction and interests 
Second, we build content-based classifiers for the industry prediction task and study the effect of incorporating textual features from the users' profile metadata using various meta-classification techniques, significantly improving both the overall accuracy and the average per industry accuracy 
Next, after examining which words are indicative for each industry, we build vector-space representations of word meanings and calculate one deviation for each industry, illustrating how meaning is differentiated based on the users' industries  We qualitatively examine the resulting industry-informed semantic representations of words by listing the words per industry that are most similar to job related and general interest terms 
Finally, we rank the different industries based on the normalized relative frequencies of emotionally charged words (positive and negative) and, in addition, discover that, for both genders, these frequencies do not statistically significantly correlate with an industry's gender dominance ratio 
After discussing related work in Section SECREF2 , we present the dataset used in this study in Section SECREF3   In Section SECREF4 we evaluate two feature selection methods and examine the industry inference problem using the text of the users' postings  We then augment our content-based classifier by building an ensemble that incorporates several metadata classifiers  We list the most industry indicative words and expose how each industrial semantic field varies with respect to a variety of terms in Section SECREF5   We explore how the frequencies of emotionally charged words in each gender correlate with the industries and their respective gender dominance ratio and, finally, conclude in Section SECREF6  
Related Work
Alongside the wide adoption of social media by the public, researchers have been leveraging the newly available data to create and refine models of users' behavior and profiling  There exists a myriad research that analyzes language in order to profile social media users  Some studies sought to characterize users' personality BIBREF9 , BIBREF10 , while others sequenced the expressed emotions BIBREF11 , studied mental disorders BIBREF12 , and the progression of health conditions BIBREF13   At the same time, a number of researchers sought to predict the social media users' age and/or gender BIBREF14 , BIBREF15 , BIBREF16 , while others targeted and analyzed the ethnicity, nationality, and race of the users BIBREF17 , BIBREF18 , BIBREF19   One of the profile fields that has drawn a great deal of attention is the location of a user  Among others, Hecht et al  Hecht11 predicted Twitter users' locations using machine learning on nationwide and state levels  Later, Han et al  Han14 identified location indicative words to predict the location of Twitter users down to the city level 
As a separate line of research, a number of studies have focused on discovering the political orientation of users BIBREF15 , BIBREF20 , BIBREF21   Finally, Li et al  Li14a proposed a way to model major life events such as getting married, moving to a new place, or graduating  In a subsequent study, BIBREF22 described a weakly supervised information extraction method that was used in conjunction with social network information to identify the name of a user's spouse, the college they attended, and the company where they are employed 
The line of work that is most closely related to our research is the one concerned with understanding the relation between people's language and their industry  Previous research from the fields of psychology and economics have explored the potential for predicting one's occupation from their ability to use math and verbal symbols BIBREF23 and the relationship between job-types and demographics BIBREF24   More recently, Huang et al  Huang15 used machine learning to classify Sina Weibo users to twelve different platform-defined occupational classes highlighting the effect of homophily in user interactions  This work examined only users that have been verified by the Sina Weibo platform, introducing a potential bias in the resulting dataset  Finally, Preotiuc-Pietro et al  Preoctiuc15 predicted the occupational class of Twitter users using the Standard Occupational Classification (SOC) system, which groups the different jobs based on skill requirements  In that work, the data collection process was limited to only users that specifically mentioned their occupation in their self-description in a way that could be directly mapped to a SOC occupational class  The mapping between a substring of their self-description and a SOC occupational class was done manually  Because of the manual annotation step, their method was not scalable; moreover, because they identified the occupation class inside a user self-description, only a very small fraction of the Twitter users could be included (in their case, 5,191 users) 
Both of these recent studies are based on micro-blogging platforms, which inherently restrict the number of characters that a post can have, and consequently the way that users can express themselves 
Moreover, both studies used off-the-shelf occupational taxonomies (rather than self-declared occupation categories), resulting in classes that are either too generic (e g , media, welfare and electronic are three of the twelve Sina Weibo categories), or too intermixed (e g , an assistant accountant is in a different class from an accountant in SOC)  To address these limitations, we investigate the industry prediction task in a large blog corpus consisting of over 20K American users, 40K web-blogs, and 560K blog posts 
Dataset
We compile our industry-annotated dataset by identifying blogger profiles located in the U S  on the profile finder on http://www blogger com, and scraping only those users that had the industry profile element completed 
For each of these bloggers, we retrieve all their blogs, and for each of these blogs we download the 21 most recent blog postings  We then clean these blog posts of HTML tags and tokenize them, and drop those bloggers whose cumulative textual content in their posts is less than 600 characters  Following these guidelines, we identified all the U

ground_truth: small BERT

small BERT

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. What are the baselines?
Context 2: Introduction
This work is licenced under a Creative Commons Attribution 4 0 International License  License details: http://creativecommons org/licenses/by/4 0/ Deep neural networks have been widely used in text classification and have achieved promising results BIBREF0 , BIBREF1 , BIBREF2   Most focus on content information and use models such as convolutional neural networks (CNN) BIBREF3 or recursive neural networks BIBREF4   However, for user-generated posts on social media like Facebook or Twitter, there is more information that should not be ignored  On social media platforms, a user can act either as the author of a post or as a reader who expresses his or her comments about the post 
In this paper, we classify posts taking into account post authorship, likes, topics, and comments  In particular, users and their “likes” hold strong potential for text mining  For example, given a set of posts that are related to a specific topic, a user's likes and dislikes provide clues for stance labeling  From a user point of view, users with positive attitudes toward the issue leave positive comments on the posts with praise or even just the post's content; from a post point of view, positive posts attract users who hold positive stances  We also investigate the influence of topics: different topics are associated with different stance labeling tendencies and word usage  For example we discuss women's rights and unwanted babies on the topic of abortion, but we criticize medicine usage or crime when on the topic of marijuana BIBREF5   Even for posts on a specific topic like nuclear power, a variety of arguments are raised: green energy, radiation, air pollution, and so on  As for comments, we treat them as additional text information  The arguments in the comments and the commenters (the users who leave the comments) provide hints on the post's content and further facilitate stance classification 
In this paper, we propose the user-topic-comment neural network (UTCNN), a deep learning model that utilizes user, topic, and comment information  We attempt to learn user and topic representations which encode user interactions and topic influences to further enhance text classification, and we also incorporate comment information  We evaluate this model on a post stance classification task on forum-style social media platforms  The contributions of this paper are as follows: 1  We propose UTCNN, a neural network for text in modern social media channels as well as legacy social media, forums, and message boards — anywhere that reveals users, their tastes, as well as their replies to posts  2  When classifying social media post stances, we leverage users, including authors and likers  User embeddings can be generated even for users who have never posted anything  3  We incorporate a topic model to automatically assign topics to each post in a single topic dataset  4  We show that overall, the proposed method achieves the highest performance in all instances, and that all of the information extracted, whether users, topics, or comments, still has its contributions 
Extra-Linguistic Features for Stance Classification
In this paper we aim to use text as well as other features to see how they complement each other in a deep learning model  In the stance classification domain, previous work has showed that text features are limited, suggesting that adding extra-linguistic constraints could improve performance BIBREF6 , BIBREF7 , BIBREF8   For example, Hasan and Ng as well as Thomas et al  require that posts written by the same author have the same stance BIBREF9 , BIBREF10   The addition of this constraint yields accuracy improvements of 1–7% for some models and datasets  Hasan and Ng later added user-interaction constraints and ideology constraints BIBREF7 : the former models the relationship among posts in a sequence of replies and the latter models inter-topic relationships, e g , users who oppose abortion could be conservative and thus are likely to oppose gay rights 
For work focusing on online forum text, since posts are linked through user replies, sequential labeling methods have been used to model relationships between posts  For example, Hasan and Ng use hidden Markov models (HMMs) to model dependent relationships to the preceding post BIBREF9 ; Burfoot et al  use iterative classification to repeatedly generate new estimates based on the current state of knowledge BIBREF11 ; Sridhar et al  use probabilistic soft logic (PSL) to model reply links via collaborative filtering BIBREF12   In the Facebook dataset we study, we use comments instead of reply links  However, as the ultimate goal in this paper is predicting not comment stance but post stance, we treat comments as extra information for use in predicting post stance 
Deep Learning on Extra-Linguistic Features
In recent years neural network models have been applied to document sentiment classification BIBREF13 , BIBREF4 , BIBREF14 , BIBREF15 , BIBREF2   Text features can be used in deep networks to capture text semantics or sentiment  For example, Dong et al  use an adaptive layer in a recursive neural network for target-dependent Twitter sentiment analysis, where targets are topics such as windows 7 or taylor swift BIBREF16 , BIBREF17 ; recursive neural tensor networks (RNTNs) utilize sentence parse trees to capture sentence-level sentiment for movie reviews BIBREF4 ; Le and Mikolov predict sentiment by using paragraph vectors to model each paragraph as a continuous representation BIBREF18   They show that performance can thus be improved by more delicate text models 
Others have suggested using extra-linguistic features to improve the deep learning model  The user-word composition vector model (UWCVM) BIBREF19 is inspired by the possibility that the strength of sentiment words is user-specific; to capture this they add user embeddings in their model  In UPNN, a later extension, they further add a product-word composition as product embeddings, arguing that products can also show different tendencies of being rated or reviewed BIBREF20   Their addition of user information yielded 2–10% improvements in accuracy as compared to the above-mentioned RNTN and paragraph vector methods  We also seek to inject user information into the neural network model  In comparison to the research of Tang et al  on sentiment classification for product reviews, the difference is two-fold  First, we take into account multiple users (one author and potentially many likers) for one post, whereas only one user (the reviewer) is involved in a review  Second, we add comment information to provide more features for post stance classification  None of these two factors have been considered previously in a deep learning model for text stance classification  Therefore, we propose UTCNN, which generates and utilizes user embeddings for all users — even for those who have not authored any posts — and incorporates comments to further improve performance 
Method
In this section, we first describe CNN-based document composition, which captures user- and topic-dependent document-level semantic representation from word representations  Then we show how to add comment information to construct the user-topic-comment neural network (UTCNN) 
User- and Topic-dependent Document Composition
As shown in Figure FIGREF4 , we use a general CNN BIBREF3 and two semantic transformations for document composition   We are given a document with an engaged user INLINEFORM0 , a topic INLINEFORM1 , and its composite INLINEFORM2 words, each word INLINEFORM3 of which is associated with a word embedding INLINEFORM4 where INLINEFORM5 is the vector dimension  For each word embedding INLINEFORM6 , we apply two dot operations as shown in Equation EQREF6 : DISPLAYFORM0
where INLINEFORM0 models the user reading preference for certain semantics, and INLINEFORM1 models the topic semantics; INLINEFORM2 and INLINEFORM3 are the dimensions of transformed user and topic embeddings respectively  We use INLINEFORM4 to model semantically what each user prefers to read and/or write, and use INLINEFORM5 to model the semantics of each topic  The dot operation of INLINEFORM6 and INLINEFORM7 transforms the global representation INLINEFORM8 to a user-dependent representation  Likewise, the dot operation of INLINEFORM9 and INLINEFORM10 transforms INLINEFORM11 to a topic-dependent representation 
After the two dot operations on INLINEFORM0 , we have user-dependent and topic-dependent word vectors INLINEFORM1 and INLINEFORM2 , which are concatenated to form a user- and topic-dependent word vector INLINEFORM3   Then the transformed word embeddings INLINEFORM4 are used as the CNN input  Here we apply three convolutional layers on the concatenated transformed word embeddings INLINEFORM5 : DISPLAYFORM0
where INLINEFORM0 is the index of words; INLINEFORM1 is a non-linear activation function (we use INLINEFORM2 ); INLINEFORM5 is the convolutional filter with input length INLINEFORM6 and output length INLINEFORM7 , where INLINEFORM8 is the window size of the convolutional operation; and INLINEFORM9 and INLINEFORM10 are the output and bias of the convolution layer INLINEFORM11 , respectively  In our experiments, the three window sizes INLINEFORM12 in the three convolution layers are one, two, and three, encoding unigram, bigram, and trigram semantics accordingly 
After the convolutional layer, we add a maximum pooling layer among convolutional outputs to obtain the unigram, bigram, and trigram n-gram representations  This is succeeded by an average pooling layer for an element-wise average of the three maximized convolution outputs 
UTCNN Model Description
Figure FIGREF10 illustrates the UTCNN model  As more than one user may interact with a given post, we first add a maximum pooling layer after the user matrix embedding layer and user vector embedding layer to form a moderator matrix embedding INLINEFORM0 and a moderator vector embedding INLINEFORM1 for moderator INLINEFORM2 respectively, where INLINEFORM3 is used for the semantic transformation in the document composition process, as mentioned in the previous section  The term moderator here is to denote the pseudo user who provides the overall semantic/sentiment of all the engaged users for one document  The embedding INLINEFORM4 models the moderator stance preference, that is, the pattern of the revealed user stance: whether a user is willing to show his preference, whether a user likes to show impartiality with neutral statements and reasonable arguments, or just wants to show strong support for one stance  Ideally, the latent user stance is modeled by INLINEFORM5 for each user  Likewise, for topic information, a maximum pooling layer is added after the topic matrix embedding layer and topic vector embedding layer to form a joint topic matrix embedding INLINEFORM6 and a joint topic vector embedding INLINEFORM7 for topic INLINEFORM8 respectively, where INLINEFORM9 models the semantic transformation of topic INLINEFORM10 as in users and INLINEFORM11 models the topic stance tendency  The latent topic stance is also modeled by INLINEFORM12 for each topic 
As for comments, we view them as short documents with authors only but without likers nor their own comments  Therefore we apply document composition on comments although here users are commenters (users who comment)  It is noticed that the word embeddings INLINEFORM0 for the same word in the posts and comments are the same, but after being transformed to INLINEFORM1 in the document composition process shown in Figure FIGREF4 , they might become different because of their different engaged users  The output comment representation together with the commenter vector embedding INLINEFORM2 and topic vector embedding INLINEFORM3 are concatenated and a maximum pooling layer is added to select the most important feature for comments  Instead of requiring that the comment stance agree with the post, UTCNN simply extracts the most important features of the comment contents; they could be helpful, whether they show obvious agreement or disagreement  Therefore when combining comment information here, the maximum pooling layer is more appropriate than other pooling or merging layers  Indeed, we believe this is one reason for UTCNN's performance gains 
Finally, the pooled comment representation, together with user vector embedding INLINEFORM0 , topic vector embedding INLINEFORM1 , and document representation are fed to a fully connected network, and softmax is applied to yield the final stance label prediction for the post 
Experiment
We start with the experimental dataset and then describe the training process as well as the implementation of the baselines  We also implement several variations to reveal the effects of features: authors, likers, comment, and commenters  In the results section we compare our model with related work 
Dataset
We tested the proposed UTCNN on two different datasets: FBFans and CreateDebate  FBFans is a privately-owned, single-topic, Chinese, unbalanced, social media dataset, and CreateDebate is a public, multiple-topic, English, balanced, forum dataset  Results using these two datasets show the applicability and superiority for different topics, languages, data distributions, and platforms 
The FBFans dataset contains data from anti-nuclear-power Chinese Facebook fan groups from September 2013 to August 2014, including posts and their author and liker IDs  There are a total of 2,496 authors, 505,137 likers, 33,686 commenters, and 505,412 unique users  Two annotators were asked to take into account only the post content to label the stance of the posts in the whole dataset as supportive, neutral, or unsupportive (hereafter denoted as Sup, Neu, and Uns)  Sup/Uns posts were those in support of or against anti-reconstruction; Neu posts were those evincing a neutral standpoint on the topic, or were irrelevant  Raw agreement between annotators is 0 91, indicating high agreement  Specifically, Cohen’s Kappa for Neu and not Neu labeling is 0 58 (moderate), and for Sup or Uns labeling is 0 84 (almost perfect)  Posts with inconsistent labels were filtered out, and the development and testing sets were randomly selected from what was left  Posts in the development and testing sets involved at least one user who appeared in the training set  The number of posts for each stance is shown on the left-hand side of Table TABREF12   About twenty percent of the posts were labeled with a stance, and the number of supportive (Sup) posts was much larger than that of the unsupportive (Uns) ones: this is thus highly skewed data, which complicates stance classification  On average, 161 1 users were involved in one post  The maximum was 23,297 and the minimum was one (the author)  For comments, on average there were 3 comments per post  The maximum was 1,092 and the minimum was zero 
To test whether the assumption of this paper – posts attract users who hold the same stance to like them – is reliable, we examine the likes from authors of different stances  Posts in FBFans dataset are used for this analysis  We calculate the like statistics of each distinct author from these 32,595 posts  As the numbers of authors in the Sup, Neu and Uns stances are largely imbalanced, these numbers are normalized by the number of users of each stance  Table TABREF13 shows the results  Posts with stances (i e , not neutral) attract users of the same stance  Neutral posts also attract both supportive and neutral users, like what we observe in supportive posts, but just the neutral posts can attract even more neutral likers  These results do suggest that users prefer posts of the same stance, or at least posts of no obvious stance which might cause annoyance when reading, and hence support the user modeling in our approach 
The CreateDebate dataset was collected from an English online debate forum discussing four topics: abortion (ABO), gay rights (GAY), Obama (OBA), and marijuana (MAR)  The posts are annotated as for (F) and against (A)  Replies to posts in this dataset are also labeled with stance and hence use the same data format as posts  The labeling results are shown in the right-hand side of Table TABREF12   We observe that the dataset is more balanced than the FBFans dataset  In addition, there are 977 unique users in the dataset  To compare with Hasan and Ng's work, we conducted five-fold cross-validation and present the annotation results as the average number of all folds BIBREF9 , BIBREF5  
The FBFans dataset has more integrated functions than the CreateDebate dataset; thus our model can utilize all linguistic and extra-linguistic features  For the CreateDebate dataset, on the other hand, the like and comment features are not available (as there is a stance label for each reply, replies are evaluated as posts as other previous work) but we still implemented our model using the content, author, and topic information 
Settings
In the UTCNN training process, cross-entropy was used as the loss function and AdaGrad as the optimizer  For FBFans dataset, we learned the 50-dimensional word embeddings on the whole dataset using GloVe BIBREF21 to capture the word semantics; for CreateDebate dataset we used the publicly available English 50-dimensional word embeddings, pre-trained also using GloVe  These word embeddings were fixed in the training process  The learning rate was set to 0 03  All user and topic embeddings were randomly initialized in the range of [-0 1 0 1]  Matrix embeddings for users and topics were sized at 250 ( INLINEFORM0 ); vector embeddings for users and topics were set to length 10 
We applied the LDA topic model BIBREF22 on the FBFans dataset to determine the latent topics with which to build topic embeddings, as there is only one general known topic: nuclear power plants  We learned 100 latent topics and assigned the top three topics for each post  For the CreateDebate dataset, which itself constitutes four topics, the topic labels for posts were used directly without additionally applying LDA 
For the FBFans data we report class-based f-scores as well as the macro-average f-score ( INLINEFORM0 ) shown in equation EQREF19   DISPLAYFORM0
where INLINEFORM0 and INLINEFORM1 are the average precision and recall of the three class  We adopted the macro-average f-score as the evaluation metric for the overall performance because (1) the experimental dataset is severely imbalanced, which is common for contentious issues; and (2) for stance classification, content in minor-class posts is usually more important for further applications  For the CreateDebate dataset, accuracy was adopted as the evaluation metric to compare the results with related work BIBREF7 , BIBREF9 , BIBREF12  
Baselines
We pit our model against the following baselines: 1) SVM with unigram, bigram, and trigram features, which is a standard yet rather strong classifier for text features; 2) SVM with average word embedding, where a document is represented as a continuous representation by averaging the embeddings of the composite words; 3) SVM with average transformed word embeddings (the INLINEFORM0 in equation EQREF6 ), where a document is represented as a continuous representation by averaging the transformed embeddings of the composite words; 4) two mature deep learning models on text classification, CNN BIBREF3 and Recurrent Convolutional Neural Networks (RCNN) BIBREF0 , where the hyperparameters are based on their work; 5) the above SVM and deep learning models with comment information; 6) UTCNN without user information, representing a pure-text CNN model where we use the same user matrix and user embeddings INLINEFORM1 and INLINEFORM2 for each user; 7) UTCNN without the LDA model, representing how UTCNN works with a single-topic dataset; 8) UTCNN without comments, in which the model predicts the stance label given only user and topic information  All these models were trained on the training set, and parameters as well as the SVM kernel selections (linear or RBF) were fine-tuned on the development set  Also, we adopt oversampling on SVMs, CNN and RCNN because the FBFans dataset is highly imbalanced 
Results on FBFans Dataset
In Table TABREF22 we show the results of UTCNN and the baselines on the FBFans dataset  Here Majority yields good performance on Neu since FBFans is highly biased to the neutral class  The SVM models perform well on Sup and Neu but perform poorly for Uns, showing that content information in itself is insufficient to predict stance labels, especially for the minor class  With the transformed word embedding feature, SVM can achieve comparable performance as SVM with n-gram feature  However, the much fewer feature dimension of the transformed word embedding makes SVM with word embeddings a more efficient choice for modeling the large scale social media dataset  For the CNN and RCNN models, they perform slightly better than most of the SVM models but still, the content information is insufficient to achieve a good performance on the Uns posts  As to adding comment information to these models, since the commenters do not always hold the same stance as the author, simply adding comments and post contents together merely adds noise to the model 
Among all UTCNN variations, we find that user information is most important, followed by topic and comment information  UTCNN without user information shows results similar to SVMs — it does well for Sup and Neu but detects no Uns  Its best f-scores on both Sup and Neu among all methods show that with enough training data, content-based models can perform well; at the same time, the lack of user information results in too few clues for minor-class posts to either predict their stance directly or link them to other users and posts for improved performance  The 17 5% improvement when adding user information suggests that user information is especially useful when the dataset is highly imbalanced  All models that consider user information predict the minority class successfully  UCTNN without topic information works well but achieves lower performance than the full UTCNN model  The 4 9% performance gain brought by LDA shows that although it is satisfactory for single topic datasets, adding that latent topics still benefits performance: even when we are discussing the same topic, we use different arguments and supporting evidence  Lastly, we get 4 8% improvement when adding comment information and it achieves comparable performance to UTCNN without topic information, which shows that comments also benefit performance  For platforms where user IDs are pixelated or otherwise hidden, adding comments to a text model still improves performance  In its integration of user, content, and comment information, the full UTCNN produces the highest f-scores on all Sup, Neu, and Uns stances among models that predict the Uns class, and the highest macro-average f-score overall  This shows its ability to balance a biased dataset and supports our claim that UTCNN successfully bridges content and user, topic, and comment information for stance classification on social media text  Another merit of UTCNN is that it does not require a balanced training data  This is supported by its outperforming other models though no oversampling technique is applied to the UTCNN related experiments as shown in this paper  Thus we can conclude that the user information provides strong clues and it is still rich even in the minority class 
We also investigate the semantic difference when a user acts as an author/liker or a commenter  We evaluated a variation in which all embeddings from the same user were forced to be identical (this is the UTCNN shared user embedding setting in Table TABREF22 )  This setting yielded only a 2 5% improvement over the model without comments, which is not statistically significant  However, when separating authors/likers and commenters embeddings (i e , the UTCNN full model), we achieved much greater improvements (4 8%)  We attribute this result to the tendency of users to use different wording for different roles (for instance author vs commenter)  This is observed when the user, acting as an author, attempts to support her argument against nuclear power by using improvements in solar power; when acting as a commenter, though, she interacts with post contents by criticizing past politicians who supported nuclear power or by arguing that the proposed evacuation plan in case of a nuclear accident is ridiculous  Based on this finding, in the final UTCNN setting we train two user matrix embeddings for one user: one for the author/liker role and the other for the commenter role 
Results on CreateDebate Dataset
Table TABREF24 shows the results of UTCNN, baselines as we implemented on the FBFans datset and related work on the CreateDebate dataset  We do not adopt oversampling on these models because the CreateDebate dataset is almost balanced  In previous work, integer linear programming (ILP) or linear-chain conditional random fields (CRFs) were proposed to integrate text features, author, ideology, and user-interaction constraints, where text features are unigram, bigram, and POS-dependencies; the author constraint tends to require that posts from the same author for the same topic hold the same stance; the ideology constraint aims to capture inferences between topics for the same author; the user-interaction constraint models relationships among posts via user interactions such as replies BIBREF7 , BIBREF9  
The SVM with n-gram or average word embedding feature performs just similar to the majority  However, with the transformed word embedding, it achieves superior results  It shows that the learned user and topic embeddings really capture the user and topic semantics  This finding is not so obvious in the FBFans dataset and it might be due to the unfavorable data skewness for SVM  As for CNN and RCNN, they perform slightly better than most SVMs as we found in Table TABREF22 for FBFans 
Compared to the ILP BIBREF7 and CRF BIBREF9 methods, the UTCNN user embeddings encode author and user-interaction constraints, where the ideology constraint is modeled by the topic embeddings and text features are modeled by the CNN  The significant improvement achieved by UTCNN suggests the latent representations are more effective than overt model constraints 
The PSL model BIBREF12 jointly labels both author and post stance using probabilistic soft logic (PSL) BIBREF23 by considering text features and reply links between authors and posts as in Hasan and Ng's work  Table TABREF24 reports the result of their best AD setting, which represents the full joint stance/disagreement collective model on posts and is hence more relevant to UTCNN  In contrast to their model, the UTCNN user embeddings represent relationships between authors, but UTCNN models do not utilize link information between posts  Though the PSL model has the advantage of being able to jointly label the stances of authors and posts, its performance on posts is lower than the that for the ILP or CRF models  UTCNN significantly outperforms these models on posts and has the potential to predict user stances through the generated user embeddings 
For the CreateDebate dataset, we also evaluated performance when not using topic embeddings or user embeddings; as replies in this dataset are viewed as posts, the setting without comment embeddings is not available  Table TABREF24 shows the same findings as Table TABREF22 : the 21% improvement in accuracy demonstrates that user information is the most vital  This finding also supports the results in the related work: user constraints are useful and can yield 11 2% improvement in accuracy BIBREF7   Further considering topic information yields 3 4% improvement, suggesting that knowing the subject of debates provides useful information  In sum, Table TABREF22 together with Table TABREF24 show that UTCNN achieves promising performance regardless of topic, language, data distribution, and platform 
Conclusion
We have proposed UTCNN, a neural network model that incorporates user, topic, content and comment information for stance classification on social media texts  UTCNN learns user embeddings for all users with minimum active degree, i e , one post or one like  Topic information obtained from the topic model or the pre-defined labels further improves the UTCNN model  In addition, comment information provides additional clues for stance classification  We have shown that UTCNN achieves promising and balanced results  In the future we plan to explore the effectiveness of the UTCNN user embeddings for author stance classification 
Acknowledgements
Research of this paper was partially supported by Ministry of Science and Technology, Taiwan, under the contract MOST 104-2221-E-001-024-MY2 
Context 3: S  bloggers with completed industry information 
Traditionally, standardized industry taxonomies organize economic activities into groups based on similar production processes, products or services, delivery systems or behavior in financial markets  Following such assumptions and regardless of their many similarities, a tomato farmer would be categorized into a distinct industry from a tobacco farmer  As demonstrated in Preotiuc-Pietro et al  Preoctiuc15 such groupings can cause unwarranted misclassifications 
The Blogger platform provides a total of 39 different industry options  Even though a completed industry value is an implicit text annotation, we acknowledge the same problem noted in previous studies: some categories are too broad, while others are very similar  To remedy this and following Guibert et al  Guibert71, who argued that the denominations used in a classification must reflect the purpose of the study, we group the different Blogger industries based on similar educational background and similar technical terminology  To do that, we exclude very general categories and merge conceptually similar ones  Examples of broad categories are the Education and the Student options: a teacher could be teaching in any concentration, while a student could be enrolled in any discipline  Examples of conceptually similar categories are the Investment Banking and the Banking options 
The final set of categories is shown in Table TABREF1 , along with the number of users in each category  The resulting dataset consists of 22,880 users, 41,094 blogs, and 561,003 posts  Table TABREF2 presents additional statistics of our dataset 
Text-based Industry Modeling
After collecting our dataset, we split it into three sets: a train set, a development set, and a test set  The sizes of these sets are 17,880, 2,500, and 2,500 users, respectively, with users randomly assigned to these sets  In all the experiments that follow, we evaluate our classifiers by training them on the train set, configure the parameters and measure performance on the development set, and finally report the prediction accuracy and results on the test set  Note that all the experiments are performed at user level, i e , all the data for one user is compiled into one instance in our data sets 
To measure the performance of our classifiers, we use the prediction accuracy  However, as shown in Table TABREF1 , the available data is skewed across categories, which could lead to somewhat distorted accuracy numbers depending on how well a model learns to predict the most populous classes  Moreover, accuracy alone does not provide a great deal of insight into the individual performance per industry, which is one of the main objectives in this study  Therefore, in our results below, we report: (1) micro-accuracy ( INLINEFORM0 ), calculated as the percentage of correctly classified instances out of all the instances in the development (test) data; and (2) macro-accuracy ( INLINEFORM1 ), calculated as the average of the per-category accuracies, where the per-category accuracy is the percentage of correctly classified instances out of the instances belonging to one category in the development (test) data 
Leveraging Blog Content
In this section, we seek the effectiveness of using solely textual features obtained from the users' postings to predict their industry 
The industry prediction baseline Majority is set by discovering the most frequently featured class in our training set and picking that class in all predictions in the respective development or testing set 
After excluding all the words that are not used by at least three separate users in our training set, we build our AllWords model by counting the frequencies of all the remaining words and training a multinomial Naive Bayes classifier  As seen in Figure FIGREF3 , we can far exceed the Majority baseline performance by incorporating basic language signals into machine learning algorithms (173% INLINEFORM0 improvement) 
We additionally explore the potential of improving our text classification task by applying a number of feature ranking methods and selecting varying proportions of top ranked features in an attempt to exclude noisy features  We start by ranking the different features, w, according to their Information Gain Ratio score (IGR) with respect to every industry, i, and training our classifier using different proportions of the top features  INLINEFORM0 INLINEFORM1
Even though we find that using the top 95% of all the features already exceeds the performance of the All Words model on the development data, we further experiment with ranking our features with a more aggressive formula that heavily promotes the features that are tightly associated with any industry category  Therefore, for every word in our training set, we define our newly introduced ranking method, the Aggressive Feature Ranking (AFR), as: INLINEFORM0
In Figure FIGREF3 we illustrate the performance of all four methods in our industry prediction task on the development data  Note that for each method, we provide both the accuracy ( INLINEFORM0 ) and the average per-class accuracy ( INLINEFORM1 )  The Majority and All Words methods apply to all the features; therefore, they are represented as a straight line in the figure  The IGR and AFR methods are applied to varying subsets of the features using a 5% step 
Our experiments demonstrate that the word choice that the users make in their posts correlates with their industry  The first observation in Figure FIGREF3 is that the INLINEFORM0 is proportional to INLINEFORM1 ; as INLINEFORM2 increases, so does INLINEFORM3   Secondly, the best result on the development set is achieved by using the top 90% of the features using the AFR method  Lastly, the improvements of the IGR and AFR feature selections are not substantially better in comparison to All Words (at most 5% improvement between All Words and AFR), which suggest that only a few noisy features exist and most of the words play some role in shaping the “language" of an industry 
As a final evaluation, we apply on the test data the classifier found to work best on the development data (AFR feature selection, top 90% features), for an INLINEFORM0 of 0 534 and INLINEFORM1 of 0 477 
Leveraging User Metadata
Together with the industry information and the most recent postings of each blogger, we also download a number of accompanying profile elements  Using these additional elements, we explore the potential of incorporating users' metadata in our classifiers 
Table TABREF7 shows the different user metadata we consider together with their coverage percentage (not all users provide a value for all of the profile elements)  With the exception of the gender field, the remaining metadata elements shown in Table TABREF7 are completed by the users as a freely editable text field  This introduces a considerable amount of noise in the set of possible metadata values  Examples of noise in the occupation field include values such as “Retired”, “I work ”, or “momma” which are not necessarily informative for our industry prediction task 
To examine whether the metadata fields can help in the prediction of a user's industry, we build classifiers using the different metadata elements  For each metadata element that has a textual value, we use all the words in the training set for that field as features  The only two exceptions are the state field, which is encoded as one feature that can take one out of 50 different values representing the 50 U S  states; and the gender field, which is encoded as a feature with a distinct value for each user gender option: undefined, male, or female 
As shown in Table TABREF9 , we build four different classifiers using the multinomial NB algorithm: Occu (which uses the words found in the occupation profile element), Intro (introduction), Inter (interests), and Gloc (combined gender, city, state) 
In general, all the metadata classifiers perform better than our majority baseline ( INLINEFORM0 of 18 88%)  For the Gloc classifier, this result is in alignment with previous studies BIBREF24   However, the only metadata classifier that outperforms the content classifier is the Occu classifier, which despite missing and noisy occupation values exceeds the content classifier's performance by an absolute 3 2% 
To investigate the promise of combining the five different classifiers we have built so far, we calculate their inter-prediction agreement using Fleiss's Kappa BIBREF25 , as well as the lower prediction bounds using the double fault measure BIBREF26   The Kappa values, presented in the lower left side of Table TABREF10 , express the classification agreement for categorical items, in this case the users' industry  Lower values, especially values below 30%, mean smaller agreement  Since all five classifiers have better-than-baseline accuracy, this low agreement suggests that their predictions could potentially be combined to achieve a better accumulated result

ground_truth: SVM with unigram, bigram, and trigram features, SVM with average word embedding, SVM with average transformed word embeddings, CNN, ecurrent Convolutional Neural Networks, SVM and deep learning models with comment information

SVM with unigram, bigram, trigram features, with average word embedding, with average transformed word embeddings, CNN and RCNN, SVM, CNN, RCNN with comment information

prompt_front: You are given a scientific article and a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write "unanswerable". If the question is a yes/no question, answer "yes", "no", or "unanswerable". Do not provide any explanation.

Please answer the questions based on the text provided below. How many users do they look at?
Context 3: 
Moreover, the double fault measure values, which are presented in the top-right hand side of Table TABREF10 , express the proportion of test cases for which both of the two respective classifiers make false predictions, essentially providing the lowest error bound for the pairwise ensemble classifier performance  The lower those numbers are, the greater the accuracy potential of any meta-classification scheme that combines those classifiers  Once again, the low double fault measure values suggest potential gain from a combination of the base classifiers into an ensemble of models 
After establishing the promise of creating an ensemble of classifiers, we implement two meta-classification approaches  First, we combine our classifiers using features concatenation (or early fusion)  Starting with our content-based classifier (Text), we successively add the features derived from each metadata element  The results, both micro- and macro-accuracy, are presented in Table TABREF12   Even though all these four feature concatenation ensembles outperform the content-based classifier in the development set, they fail to outperform the Occu classifier 
Second, we explore the potential of using stacked generalization (or late fusion) BIBREF27   The base classifiers, referred to as L0 classifiers, are trained on different folds of the training set and used to predict the class of the remaining instances  Those predictions are then used together with the true label of the training instances to train a second classifier, referred to as the L1 classifier: this L1 is used to produce the final prediction on both the development data and the test data  Traditionally, stacking uses different machine learning algorithms on the same training data  However in our case, we use the same algorithm (multinomial NB) on heterogeneous data (i e , different types of data such as content, occupation, introduction, interests, gender, city and state) in order to exploit all available sources of information 
The ensemble learning results on the development set are shown in Table TABREF12   We notice a constant improvement for both metrics when adding more classifiers to our ensemble except for the Gloc classifier, which slightly reduces the performance  The best result is achieved using an ensemble of the Text, Occu, Intro, and Inter L0 classifiers; the respective performance on the test set is an INLINEFORM0 of 0 643 and an INLINEFORM1 of 0 564  Finally, we present in Figure FIGREF11 the prediction accuracy for the final classifier for each of the different industries in our test dataset  Evidently, some industries are easier to predict than others  For example, while the Real Estate and Religion industries achieve accuracy figures above 80%, other industries, such as the Banking industry, are predicted correctly in less than 17% of the time  Anecdotal evidence drawn from the examination of the confusion matrix does not encourage any strong association of the Banking class with any other  The misclassifications are roughly uniform across all other classes, suggesting that the users in the Banking industry use language in a non-distinguishing way 
Qualitative Analysis
In this section, we provide a qualitative analysis of the language of the different industries 
Top-Ranked Words
To conduct a qualitative exploration of which words indicate the industry of a user, Table TABREF14 shows the three top-ranking content words for the different industries using the AFR method 
Not surprisingly, the top ranked words align well with what we would intuitively expect for each industry  Even though most of these words are potentially used by many users regardless of their industry in our dataset, they are still distinguished by the AFR method because of the different frequencies of these words in the text of each industry 
Industry-specific Word Similarities
Next, we examine how the meaning of a word is shaped by the context in which it is uttered  In particular, we qualitatively investigate how the speakers' industry affects meaning by learning vector-space representations of words that take into account such contextual information  To achieve this, we apply the contextualized word embeddings proposed by Bamman et al  Bamman14, which are based on an extension of the “skip-gram" language model BIBREF28  
In addition to learning a global representation for each word, these contextualized embeddings compute one deviation from the common word embedding representation for each contextual variable, in this case, an industry option  These deviations capture the terms' meaning variations (shifts in the INLINEFORM0 -dimensional space of the representations, where INLINEFORM1 in our experiments) in the text of the different industries, however all the embeddings are in the same vector space to allow for comparisons to one another 
Using the word representations learned for each industry, we present in Table TABREF16 the terms in the Technology and the Tourism industries that have the highest cosine similarity with a job-related word, customers  Similarly, Table TABREF17 shows the words in the Environment and the Tourism industries that are closest in meaning to a general interest word, food  More examples are given in the Appendix SECREF8  
The terms that rank highest in each industry are noticeably different  For example, as seen in Table TABREF17 , while food in the Environment industry is similar to nutritionally and locally, in the Tourism industry the same word relates more to terms such as delicious and pastries  These results not only emphasize the existing differences in how people in different industries perceive certain terms, but they also demonstrate that those differences can effectively be captured in the resulting word embeddings 
Emotional Orientation per Industry and Gender
As a final analysis, we explore how words that are emotionally charged relate to different industries  To quantify the emotional orientation of a text, we use the Positive Emotion and Negative Emotion categories in the Linguistic Inquiry and Word Count (LIWC) dictionary BIBREF29   The LIWC dictionary contains lists of words that have been shown to correlate with the psychological states of people that use them; for example, the Positive Emotion category contains words such as “happy,” “pretty,” and “good ”
For the text of all the users in each industry we measure the frequencies of Positive Emotion and Negative Emotion words normalized by the text's length  Table TABREF20 presents the industries' ranking for both categories of words based on their relative frequencies in the text of each industry 
We further perform a breakdown per-gender, where we once again calculate the proportion of emotionally charged words in each industry, but separately for each gender  We find that the industry rankings of the relative frequencies INLINEFORM0 of emotionally charged words for the two genders are statistically significantly correlated, which suggests that regardless of their gender, users use positive (or negative) words with a relative frequency that correlates with their industry  (In other words, even if e g , Fashion has a larger number of women users, both men and women working in Fashion will tend to use more positive words than the corresponding gender in another industry with a larger number of men users such as Automotive )
Finally, motivated by previous findings of correlations between job satisfaction and gender dominance in the workplace BIBREF30 , we explore the relationship between the usage of Positive Emotion and Negative Emotion words and the gender dominance in an industry  Although we find that there are substantial gender imbalances in each industry (Appendix SECREF9 ), we did not find any statistically significant correlation between the gender dominance ratio in the different industries and the usage of positive (or negative) emotional words in either gender in our dataset 
Conclusion
In this paper, we examined the task of predicting a social media user's industry  We introduced an annotated dataset of over 20,000 blog users and applied a content-based classifier in conjunction with two feature selection methods for an overall accuracy of up to 0 534, which represents a large improvement over the majority class baseline of 0 188 
We also demonstrated how the user metadata can be incorporated in our classifiers  Although concatenation of features drawn both from blog content and profile elements did not yield any clear improvements over the best individual classifiers, we found that stacking improves the prediction accuracy to an overall accuracy of 0 643, as measured on our test dataset  A more in-depth analysis showed that not all industries are equally easy to predict: while industries such as Real Estate and Religion are clearly distinguishable with accuracy figures over 0 80, others such as Banking are much harder to predict 
Finally, we presented a qualitative analysis to provide some insights into the language of different industries, which highlighted differences in the top-ranked words in each industry, word semantic similarities, and the relative frequency of emotionally charged words 
Acknowledgments
This material is based in part upon work supported by the National Science Foundation (#1344257) and by the John Templeton Foundation (#48503)  Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation or the John Templeton Foundation 
Additional Examples of Word Similarities

ground_truth: 22,880 users

20,000