A Semantics-based Approach to Disclosure Classification in User-Generated Online Content (2020)

Abstract: As users engage in public discourse, the rate of voluntarily disclosed personal information has seen a steep increase. So-called self-disclosure can result in a number of privacy concerns. Users are often unaware of the sheer amount of personal information they share across online forums, commentaries, and social networks, as well as the power of modern AI to synthesize and gain insights from this data. This paper presents an approach to detect emotional and informational self-disclosure in natural language. We hypothesize that identifying frame semantics can meaningfully support this task. Specifically, we use Semantic Role Labeling to identify the lexical units and their semantic roles that signal self-disclosure. Experimental results on Reddit data show the performance gain of our method when compared to standard text classification methods based on BiLSTM, and BERT. In addition to improved performance, our approach provides insights into the drivers of disclosure behaviors.

## introduction with the growth of social networking sites and increased user engagement with public discourse online, heightened rates of user disclosure of personal information (henceforth, self-disclosure) have raised privacy and security concerns. prior research @xcite suggests that selfdisclosure may be more common online thanks to the relative anonymity afforded in this environment and the lack of non-verbal cues to signal thoughts or feelings. users are often unaware of the aggregate amount of personal information they share, as well as the power of modern ai to synthesize and gain insights from this data. automating the process of identification and classification of private information in text is challenging @xcite . a large volume of textual data needs to be processed, and a number of real-time requirements need to be met @xcite , @xcite , and significant ambiguities arise from nuanced use of natural language. in this work, we adopt the existing framework of semantic role labeling to support self-disclosure identification and classification. semantic role labeling (srl) is a process which aims to recognize all predicate-argument pairs along with their roles in a given sentence and its predicates (usually verbs). srl is a task with numerous applications to natural language processing (nlp) like question-answering @xcite , information extraction @xcite , machine translation @xcite , text-to-scene generation @xcite , dialog systems @xcite and social-network extraction @xcite . we hypothesize that the inclusion of semantic frames can provide valuable context for the detection of self-disclosure. our code is available here foot_0 . self-disclosure in social media can take two nonexclusive forms: emotional disclosure, in which the user reveals their feelings towards something or someone; and informational disclosure, where the user reveals objective personal information, e.g., age, career, or address. following, we propose an approach detecting emotional and informational self-disclosure in text. specifically, we leverage the structured representations of frame semantics. our method outperforms standard classification methods based on cnn, bilstm, and bert by 9% for emotional disclosure and 2% for informational disclosure. ## related work detection of private and sensitive information from user texts has been studied extensively. however, approaches to date appear to be either confined to specific application domains or targeted to specific identifying attributes. many automated methods for detection of self-disclosure rely on the presence of first-person pronouns, disregard context, and suffer from poor generalizability @xcite @xcite @xcite . fundamentally, most studies equate disclosure to the revelation of explicitly private information @xcite . we posit that this frame is insufficient to capture the breadth of victimization that can result from voluntarily shared personal information (e.g., cyberbullying @xcite ), and critically, harms supported by increasingly powerful inference algorithms operating on massive-scale longitudinal datasets (e.g., targeting, manipulation @xcite ). recent advances in language models have shown improved applicability to classification tasks. @xcite introduced a deep bidirectional transformer (bert) which provided state-of-the-art results on numerous nlp tasks @xcite . we use bert as a baseline in this paper. @xcite proposed a method to detect disclosures of private information in natural language text through linguistically-motivated artificial neural networks. however, these models do not provide insights into the drivers of disclosure. @xcite propose heuristics to predict information disclosure, but these heuristics are not exhaustive. @xcite first introduced the task of detecting the semantic frames evoked in text (semantic role labeling; srl), along with their arguments, formalized in @xcite . there are several srl annotation conventions, such as propbank @xcite and framenet @xcite . propbank provides a more general role labeling, whereas framenet provides much denser annotations with more than 1200 frame types. several studies have explored @xcite srl with deep learning techniques. @xcite shows that the semantic frames defined in framenet can be extended across languages. apart from this, several studies have applied srl features to other natural language processing tasks. @xcite adapted a framenet semantic parser for spoken language understanding using adversarial learning. @xcite used semantic parsing to generate templates for question answering tasks. @xcite used semantic role labeling to extract relations in the text without predefining domain or vocabulary. @xcite utilized the predicate-argument structure of semantic role labeling to enhance machine translation. @xcite extends the existing framenet database to bridge visual cues with semantic frames for the text-to-scene generation task. @xcite used semantic parsers to induce and fill semantic slots in dialog systems automatically. while, @xcite extract social networks from unstructured text using the framenet-defined tree kernel representations. our work is motivated in part by @xcite . authors show that bert contains elements of the natural language processing pipeline: pos tagging, parsing, ner, semantic roles, and coreference. we explore semantic role labeling specifically for the disclosure detection problem. ## frame semantics the theory of frame semantics asserts that people understand the meaning of words largely by the frames which they evoke. the frames represent story fragments, which serve to connect a group of words to a bundle of meanings; for example, the term avenger evokes the revenge frame, which describes a complex series of events and a group of participants. the study of frame semantics attempts to define frames and the "participants and props" involved in each of them. a frame is composed of lexical units with frame elements. a lexical unit (lu) is a pairing of a word with a meaning. typically, each sense of a word belongs to a different semantic frame, a script-like conceptual structure that describes a particular type of situation, object, or event along with its participants and props. for example, the apply heat frame describes a common situation involving a cook, food, and a heating instrument. these semantic roles are referred to as frame elements (fes). frame-evoking words are lus in the apply heat frame. this frame is evoked by words such as bake, blanch, boil, broil, brown, simmer, steam, etc. the framenet @xcite lexical database currently contains more than 13,000 lexical units, around 7,000 of which are hierarchically annotated. a total of approximately 1200 semantic frames are exemplified in more than 200,000 annotated sentences. ## frame semantics for disclosure detection we approach the problem of disclosure detection through the learning of semantic-role based labels common to disclosure. the intuition behind semantic role labeling is to assign semantic roles consistent with the frame semantics that are predefined in framenet @xcite database. accordingly, srl models recover the latent predicate-argument structure of a sentence. exemplar sentences and frame semantics are shown in figures 1 and 2 . target words and text spans are highlighted in the sentence, and their lexical units are shown italicized below. frames are shown in colored blocks, and frame element segments appear horizontally alongside the frame. the srl-labeled sentence in figure 1 , provides an example of a sentence containing emotional disclosure. the frame emotion active is invoked by the predicate "worried". this frame has two lexical units containing words "i" and "worried". the lexical unit "i" is assigned a semantic role of experiencer. we call experiencer a frameelement of emotion active frame. clearly, emotion active with an experiencer as "i" leads to a self-disclosure of emotion. figure 2 shows the case of multiple frames invoked by different predicates. the make agreement on action is invoked by the predicate "agree". this frame has multiple lexical units, but two frame elements. the frame element party 1 is assigned to "i" and obligation is assigned to the span "with all the other points you made". this frame support informational self-disclosure. our model predicts disclosure in a sentence based on the semantic frames present. we formulate our disclosure classification model as follows. a sentence s contains a set of semantic frames @xmath0, f 2 , . . . , f m } where @xmath1. every semantic frame f j has a frame identification i j and frame elements set e @xmath2, e j2 , . . . , e jk } where @xmath3. e is a set of pre-defined frame elements in framenet. in our formulation of the problem, the sentence s contains a disclosure if at least one of the frames f contained in s is associated with disclosure, according to our classifier. formally, ## semantic frame embedding the framenet project @xcite has developed a lexicon of more than 1,200 semantic frames, and a corpus of sentences annotated with frames. we use the framenet database to extract semantic frames from the sentences in our dataset. frame-semantic parsing is a pipeline of three sub-tasks: predicate identification (which words evoke the frames?); frame identification (which frames does each predicate evoke?); and argument (frame-element) identification (which span of the text provides possible roles from e?). target identification is usually a classification problem. for the purpose of frame semantics extraction, we use open-sesame (semi-markov softmaxmargin argument parser; @xcite ), a framework that provides a pipeline for the three steps mentioned above. open-sesame uses bi-lstm to classify whether each word in the sentence is a predicate. for each detected predicate (mapped to all possible spans in the sentence), the framework classifies the semantic frame invoked using another bi-lstm. then the framework uses segmental rnn (segrnn; @xcite ) for predicting frame-elements for the semantic frames detected in the previous step. a semantic role labeling of a sentence s is set of semantic frames @xmath4, . . . , f m } where @xmath5. every semantic frame f j has a name i j and frame elements set e @xmath6, . . . , e jk } where @xmath7. e jk is the k th frame element of j th semantic frame in the sentence s. a frame-element has a name and a span of the sentence. ## baseline 7.1 b1: bilstm model we use the text classification model bidirectional lstm as our baseline. this model is an extension of traditional lstm. bidirectional lstm trains two (instead of one) lstm on the input sequence. the first input sequence is as-is and the second is on a reversed copy of the input sequence. this provides a forward and backward context for each token in the input sentence. bilstms provide competitive performance on text classification tasks. @xcite we use glove.6b.200d embedding for the input tokens. the model is trained with an adam optimizer and a learning rate of 5e-4 for ten epochs. the results are as shown in tables 3 and 4 . as shown, informational disclosure labels are detected with higher accuracy, and achieve 0.61 f1 score, mostly due to a higher precision rate than what we report for emotional disclosure labels. ## model classification results in our model a1, we use glove.6b.200d embedding for the input tokens. the frame-semantic feature representation of each frame in the sentence is extracted, as described in section 5.1. a regular dropout layer will not help regularize the activations independently for the equivalent features in different frames. accordingly, we add a spatial 1d-dropout layer after the frame-semantic embedding layer to help regularize the model. we apply 1d convolution with 32 kernels after the dropout layers. the output of this layer is passed to a max-pool layer applied to each kernel output. finally, the output of the maxpool layer is passed to a fully-connected layer and a classification (sigmoid) layer. another variation of our model comes with replacing the glove + bilstm layers with an elmo @xcite or bert @xcite embedding. elmo is a shallow bidirectional model. bert, unlike elmo, is a deeply bidirectional model. we present two results with these layers as the contextual word embedding layers for extracting frame-semantic representations. for the bert version of our model, we take frame identification name embedding as the hidden state of the bert last layer when the input is a tokenized version of the name. the results in tables 3 and 4 indicate the performance of our model with bert embeddings. there is a marginal improvement in the f1-score compared to the model with glove embedding. ## ablation we study the effect of frame-semantic features on the classification task. our model, when compared with the bilstm model, improves the f1-score on emotional disclosure by 9% and informational disclosure by 2%. we considered the contextual word embeddings of tokens in the sentence, frame identification name, and frame elements. this ablation study is to measure the effect of frameelements on the classification task qualitatively. we use a smaller version a1-fe of our model. in this version, we remove the frame-element embeddings (from gray cells in figure 3 ) from the semantic frame feature representation. thus, framesemantic representation concatenates the embedding of a frame identification name and the predicate. the classification model remains the same as a1 (figure 4 ). our results are summarized in informational disclosure classification performs similarly with or without frame-identification features. however, there is an improvement in f1score by 2% with frame-element features. this improvement suggests that we cannot infer informational disclosure from the frame identification name alone. frame-elements are crucial for detecting informational disclosure. ## the role of conversation in self-disclosure in this section, we explore the effect of peer influence features on disclosure detection in the conversation. given the highly contextual and inherently social character of self-disclosure, we are motivated to explore peer influence as a meaningful signal for this behavior. we incorporate peer effects in models aiming to detect and predict disclosure in conversation. we touch upon some early findings in this direction. practically we develop a model that addresses the problem of predicting disclosure in a given comment using the comment text and peer influence features extracted from previous comments. we have completed an early exploration of conversational modeling of the effects of peer influence in reddit conversations. we augmented our original reddit dataset @xcite with the missing responses from the original comments using python reddit api wrapper (praw) foot_1 . we sample 1200 conversations (about 1600 users) from the comment trees and manually annotate the emotional and informational disclosure in comments using mechanical turk with consensus from three workers. we labeled the dataset into three labels: no disclosure vs low disclosure vs high disclosure for each of emotional and informational disclosure. we then calculate reliability metrics 4 which indicate high reliability scores for binary: no disclosure (no) vs disclosure (low or high) labels. type reliability metric info emo no vs low vs high fleiss kappa 0.484 0.242 gwet's ac1/ac2 0.631 0.317 binary fleiss kappa 0.653 0.394 gwet's ac1/ac2 0.701 0.644 a reddit post is composed of a post text written by an author, comments and votes. a comment is composed of comment text written by an author, reply comments and votes. recursive comments within each post form a comment tree. we sample conversations from this comment tree by recursively taking the comments where the successor comment is a direct reply to the predecessor comment. unlike the original dataset @xcite which provides annotations for sentences sampled from the comments, here we classify the entire comment. ## conclusion in this paper, we have presented a study using semantic role labels to support the detection of voluntarily disclosed private information in a usergenerated text. to the best of our knowledge, ours is the first study performing in-depth semantic analysis to facilitate detection and analysis of selfdisclosure. in doing so, we have simultaneously improved upon state-of-the-art performance for detection of disclosure in sentences and furnished meaningful semantic information about tagged disclosures. the success of frame semantics in helping to identify sentences containing disclosure is perhaps unsurprising given its power in distilling meaning from groups of individual words. yet, our models have potential for more insightful analysis, beyond what is presented here. for example, semantic frames across sentences in the comment can be linked in a graph-like structure if the same entities evoke the semantic frames. moreover, the same can be applied across comments. we will explore these graph-based approaches in future work.