Variation in framing as a function of temporal reporting distance (2021)

Abstract: In this paper, we measure variation in framing as a function of foregrounding and backgrounding in a co-referential corpus with a range of temporal distance. In one type of experiment, frame-annotated corpora grouped under event types were contrasted, resulting in a ranking of frames with typicality rates. In contrasting between publication dates, a different ranking of frames emerged for documents that are close to or far from the event instance. In the second type of analysis, we trained a diagnostic classifier with frame occurrences in order to let it differentiate documents based on their temporal distance class (close to or far from the event instance). The classifier performs above chance and outperforms models with words.

## introduction to understand streams of news and blogs in terms of the ways in which events can be framed, we need to model how these streams develop over time in relation to the common ground that is created. the common ground between interlocutors plays an essential role in how they refer to real-world event instances. 1 following pragmatic theory @xcite @xcite , when this common ground is low, the speaker, in an attempt to be cooperative, needs to be as informative as possible, using detailed and marked descriptions of the main event instance. when the common ground is high, the speaker can optimally use less marked expressions and hence background the main event instance in order to foreground related events with a higher informative value (see also the grounding principles of @xcite . the less marked expression then implicates prior knowledge of the event instance, which has become unnecessary to explicate. this is shown in the next two examples that report on instances of the same event type at different points in time (the-day-before versus aweek-ago). in example (1a), reference to the event instance is marked by using multiple indefinite expressions of different syntactic categories in reference to subevents: a shooting in which a man died. in example (1b), reference to the event instance is restricted to one definite expression last week's murder, which presupposes the event instance as shared knowledge and implicates its details. the rest of the text in the example focuses on other events. in this paper, we propose to overcome the data sparsity by merging data of event instances of the same event type to study foregrounding and backgrounding phenomena. we assume that it takes approximately the same amount of time for information, on for instance shooting events, to become common ground between members of a society. furthermore, such a specific event type activates a coherent set of conceptual properties typically used in reference @xcite ). yet, this use of reference might depend on mutual knowledge. based on the discussed pragmatic principles, we claim that both referential expressions and their meanings vary across documents with different temporal distances to event instances: over time, relevant information about the event instance is left implicit as a means to background reference to the event instance and foreground reference to novel information. in order to find evidence for our claim, we use framenet @xcite as a proxy to characterize event semantics. our prediction is that those frames typically associated with an event type, called typical frames, will also show a different foregrounding and backgrounding distribution as a function of the increased common ground. we expect that subevents of the event instance are foregrounded in texts with little temporal distance, whereas related disjoint events are expected to be foregrounded in texts with large temporal distance. this difference should be reflected by their frames. to test this hypothesis, we applied a method based on @xcite to learn frame typicality rates for event types from a large collection of news reports that were processed with an automatic frame-labeler @xcite . furthermore, we trained a linear support vector machine classifier to distinguish between referential texts with close temporal distance and further temporal distance on the basis of the typical frames evoked by the texts. we contrast this classifier against models trained on words. we provide evidence that frame distributions are learned by the classifier to perform the task, whereas this is lesser the case for word based models. our analysis of the results shows that the typical frames evoked in texts with a short temporal distance are backgrounded in texts of larger temporal distance by means of implicature. the main contributions of our work are: our results will help future systems in detecting events in texts and their framing but also help the computational modeling of pragmatics and implicatures. this paper is structured as follows. we first describe relevant past work in section 2. we then introduce our methodology in section 3. section 4 provides the results of our experiments, which we discuss in section 5. we conclude in section 6. ## background in this section, we discuss previous work that has been done with respect to event foregrounding, (2.1 framenet (2.2), temporal distance (2.3) and event corpora (2.4). ## methodology in this section, we describe the methodology used for both the between-event type and within-event type experiments. 6 this includes the resources, (3.1), data processing (3.2), contrastive analysis (3.3, hypotheses (3.4) and evaluation (3.5). ## results for the contrastive analysis between event types, for the contrastive analysis between temporal distance classes, the annotators show a cohen's kappa of .48, which is moderate. however, their judgments on the frames in the top and bottom ranking of fficf ratings in ## discussion in recall that in order to implicate shared knowledge, one uses minimal or less marked expressions. thus, if the typical frames of day 0 have become shared knowledge in day 8-30, then the writer optimally uses short and definite expressions to implicate them. such expressions then evoke a strong typical frame, an anchor frame, that is sufficient to both refer to the event type and implicate the typical frames as shared knowledge. such an anchor frame should show a high typicality score for the event type, but a low score across temporal distance classes, due to its frequent usage. killing and catastrophe in finally, the results of the diagnostic classifier in we assume that the results of our analysis can be generalized over unpredicted event types. from the onset, the common ground increases over time, affecting the pragmatic principles of foregrounding and backgrounding. thus, if we would be able to obtain enough texts for the event type storm, we would expect the variation in framing between temporal classes to only occur with this event type as well. since presidential election and music festival are rather anticipated events, the common ground at day 0 is at maximum height and build up from texts in preceding days. thus, for these event types, temporal distance classes should be determined from preceding days up until the event itself. ## conclusion in this paper, we measured variation in framing as a function of pragmatic foregrounding and backgrounding. we hypothesized that difference in common ground determine the extent to which the writer is able to background frames typically used in reference to the main event instance. we presented hdd, a corpus consisting of reference texts grouped under event types and enriched with publication dates. hdd was used to both perform fficf between event types and between temporal distance classes, and train a diagnostic classifier. the former resulted in a ranking of typical frames per event type and between classes. the linear svm to a large extent was able to differentiate documents of different temporal distance classes. frames turned out to be more informative than their predicates in training the model. yet, the diagnostic classifier prefers idiosynchratic frames for learning the hyperplane. in future work, we extend our experiments to more event types and we want to learn the specific frame-to-frame relations from the typical frames for event types. we expect to learn subevent relations from texts with short temporal distance and (causal) sequence relations from typical frames in texts with larger temporal distance.