Creator Context for Tweet Recommendation (2023)

Abstract: When discussing a tweet, people usually not only refer to the content it delivers, but also to the person behind the tweet. In other words, grounding the interpretation of the tweet in the context of its creator plays an important role in deciphering the true intent and the importance of the tweet. In this paper, we attempt to answer the question of how creator context should be used to advance tweet understanding. Specifically, we investigate the usefulness of different types of creator context, and examine different model structures for incorporating creator context in tweet modeling. We evaluate our tweet understanding models on a practical use case – recommending relevant tweets to news articles. This use case already exists in popular news apps, and can also serve as a useful assistive tool for journalists. We discover that creator context is essential for tweet understanding, and can improve application metrics by a large margin. However, we also observe that not all creator contexts are equal. Creator context can be time sensitive and noisy. Careful creator context selection and deliberate model structure design play an important role in creator context effectiveness.

## introduction linguists and philosophers have long recognized the importance of the interplay between utterance semantics and its context @xcite . for instance, the meaning of a statement such as i am standing here now can only be interpreted in the context of its speaker. as a more media-related example, news stories often implicitly refer to recent events, assuming common context among their readers (e.g. latest news on ukraine). while context is important for all media content, its importance is even more pronounced on short-form content platforms like twitter 1 . on twitter, the 1 twitter and tweets were rebranded to "x" and "posts" respectively in july 2023. however, we use the former names figure 2: tweet from elon musk 140 (and later 280) characters tweet word limits have long inspired idiosyncratic forms of communication @xcite . therefore, applying computational natural language understanding to tweets alone can be challenging (e.g. @xcite ; nguyen et al. ( 2020 )). in this paper, we particularly focus on the context of the creator of the tweet as a key to facilitate tweet understanding. creator context refers to the information about the creator which might not be present in the tweet itself. take the tweet by barack obama in figure 1 as an example. without knowing that the creator is a former us president, it is hard to estimate the relationship and the importance of this tweet with regards to the 2020 us presidential election. in another example (figure 2 ), the tweet mentions the pronoun "my", however, it is hard to tell who the "poster" is, and what event this tweet relates to, from the tweet content alone. incorporating the creator context can easily address both of these problems. moreover, knowing the creator is useful for understanding the authoritativeness and news-worthiness of the post, which is highly beneficial for downstream applications like tweet search and recommendation. accordingly, in this work, we investigate the importance of creator context for tweet understanding. as twitter is known as a channel for users to in the paper as this work was carried out prior to july 2023. the main contributions of this work are as follows: in our work, we study the reverse task of recommending related tweets to news articles. earlier work by @xcite formulates this as a classification task (relevant or irrelevant). on a human labeled dataset of 340 news, tweet pairs, they build an adaboost model using tweet-news tokenlevel similarity from a document likelihood model, and topic-level similarity from the latent dirichlet allocation model, along with 16 other handcrafted features such as publication time, tweet length, and follower count of the twitter user. in another work, @xcite build lexical retrieval models to measure the lexical similarity between tweet textual content and query news. they curate and release a human labeled dataset consisting of 100 news articles and 6230 news, tweet pairs. with this dataset, a recent work by @xcite directly apply deep retrieval models (most of them trained on the ms-marco dataset) in a zero-shot setup, in order to assess the generalization ability of deep models. unlike the small datasets used by prior works, we collect a large set of 8m news, tweet positive pairs from news articles with embedded tweets, which enables us to train a deep retrieval model. moreover, all the prior works use only the tweet text as opposed to our work of using tweet and creator context together, which brings significant gains as seen in our experiments. twitter user modeling. twitter user modeling is often studied in the context of personalized recommendation. prior works largely leverage the twitter users' authored posts for user profile modeling. most of them aggregate the posts and then build user profiles with various semantic granularity, such as token-based, entity-based, topic-based or category-based (e.g. @xcite ; @xcite ). as tweets are short, some researchers attempt to use external resources, e.g. linked urls or mined related articles @xcite to enrich the semantics of tweets. the prior works also find that the user interest is dynamically changing over time @xcite . short-term profiles built on recent tweets do not capture the complete user interests well, and all the historical tweets are needed to build a long-term profile @xcite . this is consistent with our findings (detailed in section 3.1). however, obtaining all the historical tweets is often technically impractical, especially at large scale and for real-time applications. in our work, we turn to more stable sources of creator context that can be obtained efficiently to approximate the long-term interest. ## methodology in this section, we first explore potential sources to mine creator context. we then introduce the task of recommending related tweets to news articles, and discuss how the creator context could augment tweet content for this task. ## experiments & analysis in this section, we evaluate our creator context enhanced retrieval models for recommending related tweets to a news article. we compare the performance of different types of creator context and different model structures for incorporating it. ## discussion our dataset is curated from news articles and their embedded tweets. those tweets are chosen by journalists and might be biased towards certain creators (e.g. popular figures/celebrities). we thus explore whether our model can generalize to the general population of tweets from arbitrary creators and if creator context can be useful for modeling tweets about local, rare and special-interest events. to this end, we obtain a large set of public tweets from the internet archive (detailed in appendix c). as news events are highly time sensitive, for each article, we restrict its candidate tweets to all tweets posted no earlier than one week of the article posting time (unlike the experiments in section 4 which used all tweets without any time restriction as retrieval candidates). we use our best performing early fusion model to retrieve relevant tweets and perform a small scale annotation for a quality check. to be specific, we randomly pick three dates (07/31/2017, 02/27/2018 and 06/24/2019), and then randomly sample 100 articles published on the three dates. we (three authors of this paper) annotated the relevance of top five retrieved tweets for each article. we find that our model achieves a high precision -91% of articles have at least one relevant tweet in the top five results. this demonstrates that our model could perform well on the general tweet population. please see appendix d for examples. we also find that the creators of the top retrieved results are diverse, consisting not only of popular figures but also ordinary people like local residents. see figure 9 for an example, where, for a news article about an olympic athlete returning to his home ground wisconsin, the top retrieved tweet comes from a local resident. the creator's twitter page mentions "kimberley, wisconsin" as the geo location making the creator context very useful in this context. moreover, we observe that creator understanding is particularly useful for modeling tweets about local and less popular events. figure 10 shows an example of the top tweet retrieved by our model (which is also embedded) for a news article about a local news of a vegetation fire in san josé, california. the creator's display name "san josé fire dept." and their geo location "san josé, california" are critical in matching and recommending this particular tweet for the news article. ## conclusion & future work in this paper, we investigated the problem of using creator context for tweet understanding. different types of creator context, including creators' bio, screen handle, display name, website and location are explored. we also proposed and examined three different model structures to incorporate creator context for the task of recommending tweets to news articles. we demonstrated that, with creator context, significant quality improvements can be achieved. we also showed that not all creator contexts are equal, and they have different effectiveness. for example, creators' tweets, as their topics shift quickly, when used as creator context, can adversely affect performance, especially when the tweets and news articles are not temporally aligned. as future work, we would like to explore how non-topical creator features like followers count and creator social graph could be incorporated into tweet recommendation. we also plan to investigate the usefulness of creator context for other tasks (e.g., event detection) and other platforms (e.g., tiktok, youtube shorts).