THIS ARTICLE USES WORDS OR LANGUAGE THAT IS CONSIDERED PROFANE, VULGAR, OR OFFENSIVE BY SOME READERS. The presence of a significant amount of harassment in user-generated content and its negative impact calls for robust automatic detection approaches. This requires the identification of different types of harassment. Earlier work has classified harassing language in terms of hurtfulness, abusiveness, sentiment, and profanity. However, to identify and understand harassment more accurately, it is essential to determine the contextual type that captures the interrelated conditions in which harassing language occurs. In this paper we introduce the notion of contextual type in harassment by distinguishing between five contextual types: (i) sexual, (ii) racial, (iii) appearance-related, (iv) intellectual and (v) political. We utilize an annotated corpus from Twitter distinguishing these types of harassment. We study the context of each kind to shed light on the linguistic meaning, interpretation, and distribution, with results from two lines of investigation: an extensive linguistic analysis, and the statistical distribution of uni-grams. We then build type- aware classifiers to automate the identification of type-specific harassment. Our experiments demonstrate that these classifiers provide competitive accuracy for identifying and analyzing harassment on social media. We present extensive discussion and significant observations about the effectiveness of type-aware classifiers using a detailed comparison setup, providing insight into the role of type-dependent features.
Citation: Rezvan M, Shekarpour S, Alshargi F, Thirunarayan K, Shalin VL, Sheth A (2020) Analyzing and learning the language for different types of harassment. PLoS ONE 15(3): e0227330. https://doi.org/10.1371/journal.pone.0227330
Editor: Kazutoshi Sasahara, Nagoya University, JAPAN
Received: August 17, 2018; Accepted: October 18, 2019; Published: March 27, 2020
Copyright: © 2020 Rezvan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: Data cannot be shared publicly because of Twitter data policy. Data are available from the Institutional Data Access / Ethics Committee (contact via Email) for researchers who meet the criteria for access to confidential data.
Funding: We acknowledge support from the National Science Foundation (NSF, https://www.nsf.gov) award CNS 1513721: Context-Aware Harassment Detection on Social Media. Dr. Amit Sheth, Dr. Krishnaprasad Thirunarayan, and Dr. Valerie Shalin received this award. Any opinions, findings, and conclusions, recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF. And the funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Disclaimer: This article uses words or language that is considered profane, vulgar, or offensive by some readers. Owing to the topic studied in this article, quoting offensive language is academically justified but neither we nor PLOS in any way endorse the use of these words or the content of the quotes. Likewise, the quotes do not represent our opinions or the opinions of PLOS, and we condemn online harassment and offensive language.
Although social media has enabled people to connect and interact with each other, it has also made people vulnerable to insults, humiliation, hate, bullying–facing threats from individuals who are either known (e.g., colleagues, friends) or unknown (e.g., fans, clients, anonymous entities). Please note that in this work, cyberbullying and harassment are used interchangeably. A Pew research study reports  that one-in-five (18%) victims of cyberbullying characterized their exposure as severe. The resulting negative impact from emotional distress, privacy concerns and threats to physical safety and mental health, affect individuals online and offline. This calls for tool-based, automatic detection, monitoring, and analysis of hurtful language to protect online users. The prior state-of-the-art is limited to detecting specific hurtful language such as hate speech , abusive language , and profanity , collectively termed Negative Affective Language (NAL). In the following, we present the definitions and terms for variants of harassing language:
- Hate speech is “speech that denigrates a person because of their innate and protected characteristics” . Furthermore, it is divided into two categories: directed and generalized, depending upon whether there is an explicit target or not.
- Abusive Language is “the collection and misuse of private user information, cyberbullying and the distribution of offensive, misleading, false or malicious information” .
- Offensive Language employs profanity, is strongly impolite, rude or vulgar red expressed with fighting or hurtful words to insult a targeted individual or group [7–10].
- Aggressive Language shows overt, angry and often violent social interaction with the intention of inflicting damage or other unpleasantness upon another individual or group of people [11, 12].
- Harassing (Cyberbullying) Language is the use of force, threat, or coercion to abuse, embarrass, intimidate, or aggressively dominate others. It typically denotes repeated and hostile behavior performed by a group or an individual [11–13].
These definitions are highly subjective and overlap, making them hard to differentiate. For example, the definition of harassing language is similar to aggressive language. We posit that all of these NALs are hurtful and thus harassing. But they might vary in their level of severity, presence or absence of target (victim), contextual interpretation and purpose. In this paper, we frame harassing language as offensive language where a given post/message contains “profanity, strongly impolite, rude, vulgar or threatening language”.
State-of-the-art harassment detection fails to exploit the contextual type of harassing language. Webster’s dictionary  provides the following definition for context: “the parts of a discourse that surround a word or passage and can throw light on its meaning”. Here, we describe the notion of contextual type as the linguistic or statistical conditions that help in differentiating the type of harassment. For example, the circumstance of a student who has been subjected to sexual harassment by her ex-partner differs from a student racially harassed because of her/his color. We suggest that contextual type influences the linguistic characteristics of harassment. We propose five contextual types of harassment in online communication on social media: (i) sexual harassment, (ii) racial harassment, (iii) appearance-related harassment, (iv) intellectual harassment, and (v) political harassment. This categorization is represented in Fig 1. Below, we define each type of harassment using illustrative examples from the Twitter corpus we have created.
- Sexual harassment is offensive sexual speech that usually targets females. E.g., the harasser might comment on the victim’s body in a vulgar manner or mention sexual relationships in an aggressive way. Note that using sexually profane words is not sufficient to indicate offensive sexual harassment [15, 16].
- Racial harassment targets race and ethnicity characteristics of a victim such as skin color, country of origin, culture, or religion, in an offensive manner .
- Appearance-related harassment uses embarrassing language referring to body appearance. Fat shaming  and body shaming are key subtypes of this type of harassment.
- Intellectual harassment offends the intellectual power or opinions of individuals .
- Political harassment is related to someone’s political views . Typical targets are politicians and politically inclined individuals who receive threatening messages .
Determining the real intent behind a tweet regarding the type of harassment can have serious implications for public perception. Reliable assessment of the type of harassment can have significant repercussions. We are unaware of any prior work on studying harassment concerning these five types. We summarize our contributions as follows: (i) We introduce five contextual types of harassment. Then, we provide a systematic, and comparative analysis to assess offensive language from linguistic and statistical perspectives for each contextual type. This allows us to exploit relevant features for developing classifiers to identify these critical types of harassment on social media. (ii) We develop type-aware classifiers and capture their effectiveness using a detailed comparative study. This paper is organized as follows. The next section reviews the related literature. We then present the type-aware corpus that we have developed. Subsequently, we analyze our compiled corpus linguistically as well as statistically, which shows us the significant type-specific features for various types of harassment. We then discuss supervised learning approaches and classifiers for detecting the harassing language in comparative settings. We also provide an error analysis study regarding the pitfalls and challenges of our strategy. We close with the conclusions and our future plans.
State-of-the-art in harassment research
The previous research studies targeted various social media sources such as Twitter, Instagram, and Facebook. In Table 1, we summarize the prior literature with their corresponding goals, conclusions, and underlying data sets. Here, we specifically note particularly prominent related work. In , the authors seek to predict cyberbullying incidents on Instagram. They built a predictive model for the incidence of cyberbullying using features from initially posted data, a social graph, and temporal properties. The work in  proposed an approach for detecting harassment features based on the content, sentiment, and context. Using Slashdot and MySpace data, they showed significant improvement using TFIDF supplemented with sentiment and contextual features. The authors of  proposed an approach to spotting harassers as well as victims on social media. They considered the social structure and infer which user is a likely instigator and which user is expected to be a victim. This model is based on social interactions and the language of users in social media. Similarly,  proposes a method that simultaneously discovers instigators and victims of bullying incidents. It extends an initial bullying vocabulary using twitter and ask.fm. In , the authors proposed a supervised learning method for detecting cyberbullying in Japan. In , the authors propose a supervised learning method based on fuzzy logic and genetic algorithm to identify the presence of cyberbullying terms and classify activities, such as flaming, harassment, racism, and terrorism on social media. Fuzzy rules were used to classify data, and a genetic algorithm was used for optimizing the parameters.
 explores the correlation of behaviors and actions of people and their emotions. The authors developed a large emotion-labeled dataset of harassing tweets. They applied 131 emotion hashtag keywords categorized into seven groups and collected 5 million tweets. To find useful features for emotion identification, they applied LIBLINEAR  and Multinomial Naive Bayes  algorithms. They extracted n-gram features  to analyze the emotion, and they applied Linguistic Inquiry and Word Count (LIWC) to expand the feature set with related emotional words. Interestingly, the authors of  target cyber-aggression and cyberbullying in a multi-modal context with text comments and media objects on Instagram. They concluded that non-text features are not able to substantially improve the performance of cyberbullying detection compared to text-based features.
Different from the previous work, some literature examines the psychological implications of harassment incidents . The authors in  sought the reasons behind the updates of posts on Facebook. They noticed that: (i) the majority of posts are about social activities and everyday life, (ii) people with low self-esteem updated their status on relationship whereas those with high self-esteem update their status with respect to their children. Moreover, people with narcissistic personality disorder updated their status through their achievements. Furthermore, they observed a correlation between the number of likes and comments with esteem level of people (e.g., the people with the low self-esteem receive fewer likes and comments because their status expresses greater negative affect). Similarly, the authors of  discuss narcissism personality disorder in Facebook users and its implications in harassing incidents. Our own past work [33, 34] focused on (i) using a conversation between a sender and a receiver to better capture its normal linguistic nature (e.g., base rates for curse word usage) and the nature of the relationship between participants (e.g., friends vs. strangers), and (ii) analyze comments/review threads to better identify offensive content in non-text media such as YouTube videos , to reliably detect harassment between participants.
Type-aware harassment corpus
We published a type-aware annotated corpus and lexicon in . Our corpus consists of 25,000 annotated tweets for the five types of harassment content and is available on the Git repository . In the following, we discuss our strategies for corpus compilation and annotation. The identification of cyberbullying typically begins with a lexicon of potentially profane or offensive words. We created a lexicon (compiled from online resources     ) containing offensive words covering five different types of harassment context. The resulting compiled lexicon includes six categories: (i) sexual, (ii) racial, (iii) appearance-related, (iv) intellectual, (v) political, and (vi) a generic category that contains profane words not exclusively attributed to the five specific types of harassment. A native English speaker conducted this categorization.
Corpus development and annotation
We employ Twitter as the social media data source because of its extensive public footprint. Twitter reports 313 million monthly active users that generate over 500 million tweets per day . Although the size of a tweet is restricted (140 characters at the time of corpus collection), once we consider a more extensive aggregation of tweets on a specific topic, mining approaches reveal valuable insights. We utilized the first five categories of our lexicon as seed terms for collecting tweets from Twitter API between December 18th, 2016 to January 10th 2017  (This date was close to the US presidential election. Then our political sub-corpus has many tweets with the subject of Trump). Requiring the presence of at least one lexicon item, we collected 10,000 tweets for each contextual type for a total of 50,000 tweets. As shown in Table 2, nearly half of these tweets were annotated. However, the mere presence of a lexicon item in a tweet does not assure that the tweet is harassing because the individuals might utilize these words with a different intention, e.g., in a friendly manner or as a quote. Therefore, human judges annotated the corpus to discriminate harassing tweets from non-harassing tweets. Three native English speaking annotators who were the undergraduate students with a major in computer science and minor in psychology or sociology were employed for our annotation task. The annotators determined whether or not a given tweet was harassing with respect to the type of harassment content and assigned one of three labels yes, no, and other. The last label indicates that the given tweet either does not belong to the current context or cannot be decided. Ultimately, we acquired ≈24,000 annotated tweets represented in Table 2. Note that the annotation task was done on a per tweet basis although it can be improved using the entire conversation history.
Although the annotators employed three labels, i.e., yes, no, and other, the eventual corpus excluded all tweets without a consensus label of “yes” or “no”. That is, the corpus contains only those tweets that received at least two “yes” or two “no” labels. Cohen’s kappa coefficient  measures the quality of annotation by category in Table 3. The appearance-related context shows the highest agreement rate whereas political and sexual contexts have the lowest, indicating that they are more challenging to judge due to higher ambiguity.
Annotating Golbeck corpus
The public state-of-the-art harassment-related corpus is the Golbeck corpus  that only provides generic annotation, i.e., (i) harassing and (ii) non-harassing. This corpus contains 20,428 non-redundant annotated tweets of which only 5,277 are labeled as harassing. Since we require context-aware annotations, we re-annotated the harassing tweets of Golbeck. The agreement rate (Cohen’s kappa) between the two annotators is 86%. As shown in Table 4, more than 75% of the harassing tweets are racial. This statistic confirms Golbeck’s observation. While this may be an accurate reflection of the base rate, our view is that different harassment contexts may have different consequence. An imbalanced corpus at the foundation of our research effort could result in misses of particular practical import to teenage mental health, concerning sexuality, appearance and intellect.
LIWC analysis for different types of harassment
Linguistic analysis of our corpus sheds light on the differences between the harassing corpus versus non-harassing corpus for each type. Furthermore, it provides a comparison between various types of harassment. We divided our corpus into 12 sub-corpora: (i) one generic corpus containing all harassing tweets regardless of their type, called the combined harassing corpus, (ii) one generic corpus containing all non-harassing tweets irrespective of the type called the combined non-harassing corpus, (iii) five contextual type-aware corpora including only harassing tweets per type, (iv) five contextual type-aware corpora including only non-harassing tweets per type. For linguistic analysis, we utilized LIWC  . This tool tallies 96 linguistic features using a multiword lexicon for each feature. We individually analyzed each of the 12 sub-corpora using LIWC. An effect size, statistic estimates the magnitude of an effect (e.g., mean difference, regression coefficient, Cohen’s d, and correlation coefficient)  metric was used to determine significant discriminators . Conventionally, a proportion (feature) fi is considered moderately discriminating when its effect size is more than 0.5 (i.e., ), and is considered unhelpful if . The effect size for each feature is calculated as follows: (1) where, is the mean of the experimental group on the given feature fi, is the mean of the control group wrt. the given feature fi and std is the standard deviation. For each content corpus as well as for the combined corpus, we consider the harassing corpus as the experimental group and the non-harassing corpus as the control group. We compared the prevalence of the 96 LIWC features in the harassing corpus to their prevalence in the corresponding non-harassing corpus. Out of the 96 original features, we removed features that were not significant in any of the contextual types and retained 38 of the most discriminating features as shown in Fig 2. The extreme red (green) color represents significance (regarding effect size) of the corresponding feature in the harassing (non-harassing) corpus. In the following, we highlight specific significant features to make three points. First, a feature is often diagnostic of the non-harassing corpus. Second, feature significance is type dependent. The third is related to both points: a given feature, such as “you”, can be a positive indication of harassment for one type and a negative indication of harassment for another. In the following, we indicate highly significant linguistic features derived from Fig 2 for each individual type. Note that our corpus is already biased towards curse words because curse words are present as seeds for crawling. Thus, our observations on discriminatory features are conditional on a “high recall curse word-laden corpus”.
The extreme red (green) color indicates the significance of a given feature in the harassing corpus (non-harassing corpus). E.g. the negation feature with the value 2.34 in the appearance harassing corpus is significantly higher than non-harassing corpus. The white color indicates a lack of difference for a given feature when comparing two corpora.
The pronoun “I” is prevalent in the sexually non-harassing corpus with e = −1.2, which is highly significant, e.g., i’m le***an kiss. Furthermore, the feature “MONEY” is prevalent in the harassing corpus with e = 2.9. E.g., send me free money b**ch h*es i won’t give you anything to dance to you h*e a*s industry b*t*h d*cks**king p*r*star people.
The pronoun “YOU” is prevalent in the harassing corpus with e = 0.9, e.g., Vishalp sikanda, Quideazam hahahaha u p**i can block u cant debat u p**i I***an. The “COMPARATIVE” feature is prevalent in racial non-harassing corpus with e = −0.84, e.g., save block p**i like po yung comment ni richard fronda (the word ‘like’ is an indicator of comparison in LIWC). Thus, these features can be used to discriminate between harassing and non-harassing tweets.
The pronoun “SHE” and “HE” with e = −0.9 and the pronoun “WE” with e = −0.8 are prevalent in the non-harassing corpus, e.g., realdonaldtrump putin a**hat just like word can express displeasure leader god help us (us indicates the pronoun ‘WE’). The “RISK” feature is significant in non-harassing with e = −1.9, e.g., f*** wrong democratic senators. The word ‘wrong’ represents a risk feature in LIWC dictionary. Other sample risk related words are ‘danger’, ‘doubt’, etc. Furthermore, the “ANXIETY” feature with e = −0.92 is significant in the non-harassing corpus. E.g., well i’m true dumb f*** democrat wouldn’t doubt.
“NEGATION” with e = 2.3 is prevalent in the harassing corpus (probably because of the negative language used for referring to the body and appearance-related subjects). E.g., Taylor swift cant shake c**el toe. The other significant feature in the harassing corpus is the “PAST TENSE”. E.g., Ugli a** didn’t go run yesterday get work f*t**s. Furthermore, the “COMPARATIVE” feature is prevalent in appearance-related harassing corpus with e = 0.63. E.g., hey lardass notice your look pizza perhaps like f*** salad a**hole. The word ‘like’ indicates a comparative feature.
The “FEMALE REFERENCE” feature with e = 2.3 is highly significant in intellectual harassing corpus (perhaps because girls are harassed more wrt. intellectual issues.) E.g., She is dumb f***.
Statistical analysis of different types
We investigate the relationship between the offensive words employed in collecting our corpora and the specific lexical items in the crawled corpora. We determine Q1: whether or not offensive words are observed as frequent words, Q2: whether or not the frequent words in harassing corpora differ from those in non-harassing corpora, and Q3: whether or not frequent words are type-sensitive, in other words, whether the frequent words vary with type of context. Fig 3 shows the 2D visualization of the word embeddings of the top-25 most frequent words for the harassing corpora, whereas Fig 4 represents a similar display for the top-25 most frequent words for the non-harassing corpora (the following section presents the details of word embedding). The prevalence of curse words in the non-harassing corpora is comparable to the harassing corpora. This confirms that the presence of curse words is not a sufficient indicator of harassment. In the following, we mention our key observations.
Regarding Q1, as expected, we observed that offensive words are commonplace in both harassing and non-harassing corpora across types (cf. Figs 3 and 4). In addition, we observed some emerging, frequent offensive words, such as “grab” and “camel” that can now be added to our initial offensive lexicon . Furthermore, there are frequent words that are not necessarily offensive. E.g., consider “look” or “eat” in the appearance-related type where they are implicitly related to the associated type, applicable to the appearance of a subject. Regarding Q2, we observe that the frequent words in the harassing corpora are different from those in the non-harassing corpora. The particular words in the harassing corpora also can be added to the initial lexicon of seed words. The result of this analysis can be utilized for weighting the severity of offensiveness for every single word included in our lexicon.
To reply quantitatively to Q3, we ran an annotation task on the top-15 most frequent words for each type of harassing corpus as well as the corresponding non-harassing corpus. The description of this task is as follows: we asked the human annotators (i.e., graduate students) to determine whether or not a given frequent word is related to the associated type either explicitly or implicitly. E.g., the words “eat” or “food” are implicitly related to appearance while they seem far from the type racial. The results of this exercise appear in Table 5. In the harassing corpora, the percentage of relatedness of words to the associated type is higher than 67% and in sexual and racial types, it even reaches 80%. This percentage fluctuates for non-harassing corpora. E.g., in appearance-related type, it is higher than 93% while in racial it reaches 53%. In sum, we conclude that the frequent words are mostly type-sensitive. Moreover, the prevalence of apparently offensive language in the non-harassing corpus reinforces our claim that offensive language per se is not necessarily harassing.
H stands for the harassing corpus and NH stands for the non-harassing corpus.
One caveat is that the most frequent words appearing in the sub-corpus associated with each type are predominantly stop-words or curse words, as our initial seed terms are biased to an offensive lexicon. Ignoring these words, whose presence cuts across different types of harassment, revealed that the following prominent word groups are associated with various harassment types, shedding light on the possible features that may elicit harassment: (i) In the appearance-related harassment corpus, target words such as “eat”, “ugly”, “fat”, “gym”, and “weight”, are present. (ii) In the intellectual harassment corpus, target words such as “dumb”, “stupid”, “work”, and “head”, are present. (iii) In the political harassment corpus, the target words such as “realdonaldtrump”, “libtard”, “dumb”, “touch bag”, “stupid”, and “cnn”, are present. (iv) In the racial harassment corpus, target words such as “m*ki”, “n**ger”, “b**ner”, “ch**k”, “muslim”, “i**ian”, “moron”, and “jew”, are present. (iv) In the sexual harassment corpus, target words such as “hump”, “hussy”, “l**k”, and “grab”, are present.
Predicting different types of harassing posts
We aim to develop effective supervised learning methods to detect harassing language automatically and distinguish it from non-harassing language for each contextual type. The state-of-the-art contains various approaches for detecting harassing content from non-harassing content but not for discriminating the type of harassment. We approach this gap in two ways. The first is to build individual binary classifiers that identify a particular type of harassment, e.g., a binary classifier that identifies only racial content or a binary classifier that classifies just offensive political content. The second approach uses the state-of-the-art methods to detect harassing language; after such recognition, we can employ a type-aware classifier to predict the associated type for that harassment incident. We implemented both approaches. Initially, we trained the individual classifiers for each type. In another approach, we built up a binary classifier that differentiates harassing content from non-harassing content regardless of their type. Note that any classifier from the state-of-the-art can substitute for this part. Then, we built up a multi-class classifier that predicts the type of harassment incident. The results of our experiments for both approaches reveal high accuracy. Furthermore, to verify the effectiveness of our classifier, we apply transfer learning by running our classifier on the Golbeck corpus and assess its performance for how successfully it predicts the type of harassment. In the following, we present the details of our experiments.
Transforming tweets to vectors
We utilized four approaches for transforming tweets to numerical representations (i.e., vectors): (i) the conventional vectorization approach TFIDF, (ii) word2vec, (iii) fastText and (iv) a LIWC vector. We feed our classifiers with each of these individual vectors or a combination of them.
The Term Frequency and Inverse Document Frequency (TFIDF).
We use this approach  to transform each given tweet into a weighted vector T.
Distributional semantics (i.e., word2vec and fastText).
Distributional semantics (so-called embedding models)  play a vital role in many Natural Language Processing (NLP) applications. They capture the semantics of text units (e.g., words, characters, tweets, paragraphs or documents) from the underlying corpus and represent them in a low dimensional vector space. We use two major embedding models for representing each tweet. The first one is word2vec  and the second one is fastText  . The first one learns a dense representation at the unigram level and the second one learns at the character level. Both of these approaches have two models, i.e., skip-gram model and CBOW model [53, 57] that are roughly similar. The skip-gram model (CBOW model) computes the probability of the target word wk (i.e. context word) appearing in the neighborhood of the context word wi (i.e. target word), . In this work, the vector representation of a tweet is computed as the concatenation of the vector of all tokens within the tweet. In the rest of this paper we rely on the following notations to specify a vector. W(S) and W(C) denote the low dimensional vector obtained respectively by the skip-gram model and CBOW model of the word2vec approach. F(S) and F(C) denote the low dimensional vector obtained respectively by the skip-gram model and the CBOW model of the fastText approach. We compiled a corpus containing 15,999,557 sentences from the Twitter and Leipzig Collection Corpora  leveraging our offensive lexicon presented in  as the underlying seed words. Then, we trained the embedding models on this accumulated corpus using the learning parameters reported in [53, 57]. Our dimension size equals 300, the window size is 3, and the minimum count equals 10.
Evaluation of the harassment classifiers
Preparing training datasets.
As the number of harassing tweets is not equal to the number of non-harassing ones in our corpus—in fact, it varies for each type—we prepared balanced datasets for training the classifier. We prepared five type-aware training data sets using an under-sampling approach taking all of the harassing tweets with an equal number of non-harassing (randomly sampled). Also, we prepared a combined training data set considering all of the harassing tweets regardless of their type and an equal number of non-harassing tweets. Table 6 shows the size of the training data sets for each type. Each data set contains an equal number of harassing tweets versus non-harassing tweets. Later, we employ the remaining tweets to test the robustness of the classifiers against unseen data.
Training binary classifiers.
In our experimental study, we trained four types of classifiers, using (i) Support Vector Machine (SVM) , (ii) K-Nearest Neighbors (KNN) , (iii) Gradient Boosting Machine (GBM) , and (iv) Naive Bayes (NB) . We rely on the following settings for the GBM classifier: the learning rate is 0.1, loss function is logistic regression, the number of trees is 100, sub-sample is 1.0, the criteria function is Friedman MSE, the minimum sample is 2, the minimum number of samples required to be at a leaf node is 1, and the maximum depth of the individual regression estimators is 3. We ran 10-fold cross-validation with re-sampling and iteration strategies (repeated five times). Fig 5 shows the performance of the classifiers based on an F-score measure using a TFIDF vector. Generally, the results of the NB classifier in all of the cases were inferior whereas the GBM classifier outperforms others in the majority of settings except for a few instances comparable to the SVM classifier. Thus, in the following experiments, we rely on the GBM classifier.
To gain insight over the effectiveness of various features, we feed the GBM classifier with various feature settings. The fine-grained results of our experiment are listed in Fig 6. We employed a various combination of vectors, for example, F(S)+W(S) means the input features were the skip-gram models of fastText and word2vec. In addition to the typical precision, recall and F-score measures, we provide specificity (True negative rates) and accuracy rates. We offer the following observations: (i) The tweet representation using F(S)+W(S) vector is the most effective input representation as it provides high and balanced rates for all measures including precision, recall, F-score and specificity. Note that in multiple settings such as F(S)+L+T, precision, recall, and f-score are high whereas specificity is low meaning that the classifier is biased towards one of the classes and does not perform reasonably on both classes. (ii) In the settings for which the LIWC vector L is included, typically the specificity rate is low. This probably means L vector does not provide a discriminative representation for the classifier. (iii) Generally learning the representation of tweets using the fastText approach either with skip-gram or CBOW shows high performance. This might come from the fact that encoding tweets at the character level is more effective for detecting harassment. (iv) The sexual type resulted in the classifier with the highest accuracy (with F-score 96% and specificity 94%), racial and intellectual are in the next positions (respectively with F-score 88%, 86% and specificity 83%, 79%).
Binary classifier for harassment detection.
We also trained a binary classifier on our combined corpus where it can differentiate the harassing language from non-harassing regardless of the contextual type. In situations that the type of harassment does not play a role, or type detection must occur after the harassment detection, using such a generic classifier is necessary. Table 7 shows the detailed results of this classifier in various settings of input features. Generally, the vector of FastText F shows an effective role, especially when it is coupled with the W vector; the specificity score reaches its optimum.
Type prediction using a multi-class classifier.
Apart from building binary classifiers for predicting types, we trained a multi-class classifier to predict the type of harassment incidents. We trained several multi-class classifiers, among them the GBM classifier outperformed others. Herein we report the result for GBM classifier only. We used W(S)+F(S) vectorization approach as the input feature. Then, we trained this classifier on a corpus containing all of the sub-corpora from the previous step. This corpus has samples with six various labels where five labels indicate a particular type of harassment and the last label indicates “non-harassing” implying there is no harassing language. Table 8 shows the details of the evaluation on the performance of this classifier where the micro F-score is 0.92 and the macro F-score is 0.82. Note that in the macro-level, we calculate the performances of each class and then average whereas, in the micro-level, we calculate the performance for all classes, as computing contingency table and then evaluate precision/recall and F-score . Digging into fine-grained efficiency shows that the accuracy across various classes holds similar behaviors except for a decrease in the precision and recall of the sexual type. As we will discuss in error analysis below, this type is prone to mis-classification with the other types particularly the racial type. However, comparing the performance of multi-class classifier and binary classifiers shows that the multi-class classifier mostly outperforms the binary classifiers by as much as ≈10%. Note that the accuracy of our classifier will improve on a generic tweet corpus because our current corpus has been crawled using curse words with a significantly higher proportion of harassing tweets compared to that in a generic tweets corpus, which is predominantly non-harassing and devoid of curse words. On the downside, it will miss harassment conveyed through “clean” words. However, to demonstrate the effectiveness of the current version of this classifier, in the next step we apply it on an unseen corpus to predict the type of harassment incident.
Comparison to the state-of-the-art.
Since this work was the first to introduce contextual type for harassment, comparison to the state-of-the-art that relies only on two or three variants of harassment, is unfair. However, to verify the effectiveness of our type-oriented multi-class classifier, we tested it on the harassing tweets from the Golbeck corpus (an external corpus unseen to our classifier) that is a publicly available state-of-the-art harassment-related corpus . This corpus contains 20,428 annotated tweets of which only 5,277 are labeled as harassing. It does not distinguish the nature of the harassment. In Table 4, we represented our annotations for the harassing tweets of the Golbeck corpus with respect to our types using human judges which yielded in an agreement rate of 86%. The proportion of harassing tweets per type is represented in the last column of Table 9. We ran our type-aware multi-class classifier (GMB classifier) to predict the associated type of harassing tweets on Golbeck corpus. Table 9 shows the precision, recall and F-score for each type. We observe an F-score of more than 94% for all types except for the type appearance. In the case of the racial type, the F-score reaches 98%. This high performance exceeds the state-of-the-art where they are mostly concerned about detecting the general harassing language (the reported accuracy ranges between 70% and 85%) [30, 63–65]. In addition, it shows robustness with unseen data. Note that the racial type is dominant in the Golbeck corpus. We also ran our classifier on a portion of 5,000 non-harassing tweets from Golbeck corpus, which resulted in the F-score > 98% (cf. Table 9). The last three rows of Table 9 show micro and macro precision, recall, and F-score. The closeness of the micro and macro measures shows that the classifier is not biased towards a dominant class.
To make sense of classifier errors, we examined a couple of tweets classified as sexual. E.g., for @usr you deserved to be raped by a thousand Muslims in your c**t a**hole, our classifier classified that as sexual harassment and not racial because of the word ‘rape’. Similarly, the tweet @usr @usr lol it’s not against women. It’s against f***ing feminist c***s like you. #feminazi #womenagainstfeminism was classified as sexual. Such cases are ambiguous because even manual annotation is highly subjective. In other words, categorizing harassment is highly subjective and the boundary between types is not rigid. In majority of the overlapping cases (racial and sexual), the tweets were classified as sexual rather than racial. We also analyzed errors in political tweets and concluded that harassment signal can be: (i) implicit, e.g., John Boehner blames Democrats for #shutdown. He better stop drinking cuz a few more drinks and he starts blaming the J*ws f, (ii) ambiguous ??? You’re a wh*** to the telecom industry, i hope your constituents vote you out., (iii) unreliable, e.g., It’s going to be a republican government in the US next term. Democrats can kiss their presidency bid goodbye. Let the J*ws rule!, (iv) poorly captured through annotation, e.g., the tweet @TrueNugget @FeministPeriod @OregonState Man college is becoming more and more a mistake. in the Golbeck corpus. Our classifier misses them as they are weak cases of harassment.
Our project involves analysis of Twitter data that is publicly available and that has been anonymized. It does not involve any direct interaction with any individuals or their personally identifiable data. So our work does not meet the Federal definition for human subjects research, specifically, “a systematic investigation designed to contribute to generalizable knowledge” and “research involving interaction with the individual or obtains personally identifiable private information about an individual”. Thus, this study was reviewed by the Wright State University IRB and received an exemption determination.
Conclusion and future plans
In this paper, we introduced five contextual types for harassment, namely, (i) sexual, (ii) racial, (iii) intellectual, (iv) appearance-related and (v) political. We presented experiments with a type-aware tweets corpus to analyze, learn, and understand harassing language for each type. Our contribution lies in providing a systematic and comparative approach to assessing harassing language from linguistic and statistical perspectives. Furthermore, we built type-specific classifiers, and the results of our experiments show the importance of considering the contextual type for identifying and analyzing harassment on social media.
In general, a single tweet identified as “harassing” may not provoke the same intense negative feeling that we associate with that word in the real-world scenario. However, in practice, “conversational” exchanges containing a sequence of such tweets can rise to the level of harassment causing mental and psychological anguish, and fear of physical harm. Nevertheless, our current Twitter dataset is limited to annotating single tweets in isolation for harassment. Furthermore, the reliable assessment of the type of harassment is a difficult problem because it requires significant knowledge of current events and common-sense. We plan to extend this work by learning the language of harassers as well as victims, and further study the contribution of non-verbal cues (i.e., conversational features, network features, and community features) for identifying online harassment activities, particularly on social media.
We acknowledge support from the National Science Foundation (NSF) award CNS 1513721: Context-Aware Harassment Detection on Social Media. Any opinions, findings, and conclusions, recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF.
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