Towards Open-Domain T witter User Profile Inference (2023)

Abstract: Twitter user profile inference utilizes information from Twitter to predict user attributes (e.g., occupation, location), which is controversial because of its usefulness for downstream applications and its potential to reveal users’ privacy. Therefore, it is important for researchers to determine the extent of profiling in a safe environment to facilitate proper use and make the public aware of the potential risks. Contrary to existing approaches on limited attributes, we explore open-domain Twitter user profile inference. We conduct a case study where we collect publicly available WikiData public figure profiles and use diverse WikiData predicates for profile inference. After removing sensitive attributes, our data contains over 150K public figure profiles from WikiData, over 50 different attribute predicates, and over 700K attribute values. We further propose a prompt-based generation method, which can infer values that are implicitly mentioned in the Twitter information. Experimental results show that the generation-based approach can infer more comprehensive user profiles than baseline extraction-based methods, but limitations still remain to be applied for real-world use. We also enclose a detailed ethical statement for our data, potential benefits and risks from this work, and our efforts to mitigate the risks.

## introduction users' profile information provides invaluable user features. accurate automatic user profile inference is helpful for downstream applications such as personalized search @xcite @xcite and recommendations @xcite @xcite , and computational social media analysis @xcite @xcite . however, there are increasing privacy concerns that conducting profiling without appropriate regulations may reveal people's private information. therefore, it is essential to investigate the extent of profiling to promote proper use and make the potential risks clear to public and policy makers. previous work on user profile inference has focused on a very limited set of attributes, and models for different attributes employ different strategies. one line of research has formulated it as a classification problem for attributes such as gender @xcite @xcite , age @xcite @xcite @xcite , and political polarity @xcite @xcite . in such classification settings, each attribute has it own ontology or label set, which is difficult to generalize to other attributes, especially for attributes that have many possible candidate values (e.g. geo-location, occupation). in addition, some work involves human annotation, which is expensive to be acquired and may raise fairness questions for labeled individuals @xcite . another line of research uses an extraction-based method, such as graph-based @xcite and unsupervised inference @xcite for geolocation, distant supervision-based extraction @xcite . however, they still only cover limited attributes that cannot produce comprehensive profiles. besides, many attribute values are only implicitly mentioned in twitter context, which cannot be directly extracted. in this paper, instead of limited attributes, we explore whether open-domain profiles can be effectively inferred. taking wikidata @xcite as the source of profile information, which provides a much more diverse predicate value entity id q76 name barack obama country of citizenship united states of america occupation politician position held president of the united states work location washington, d.c. spouse michelle obama ... ... (a) wikidata information. barack obama dad, husband, president, citizen. washington, dc across the country, americans are standing up for abortion rights-and i'm proud of everyone making their voices heard. join a march near you: ... happy mother's day! i hope you all let the moms and mother-figures in your life know how much they mean to you. @michelleobama, thank you for being a wonderful mother and role model to our daughters and to so many others around the world. ... we first follow @xcite to use profile information to generate distant supervised instances and build a sequence labeling-based profile extraction model, similar to @xcite . in order to allow open-domain inference, we propose to use attribute names as prompts @xcite for input sequences to capture the semantics for attribute predicates instead of involving attribute names into the tag set. however, the extraction approach requires that answers must appear in the twitter context, which ignores some implicit text clues. therefore, we further propose a promptbased generation method @xcite to infer user profiles, which can additionally produce values that are not straightforwardly mentioned in the twitter information. our statistics show that only a limited number of wikidata attribute values can be directly extracted from twitter information. our experiments demonstrate a significant improvement when using the generation-based approach compared to the extraction-based approach, indicating that performing inference instead of pure extraction will be able to obtain more information from tweets. further analysis shows that the improvement comes mainly from the power of combining extraction and inference on information not explicitly men-tioned. however, we still find several challenges and limitations for the model to be applied for realworld use, including performances of low-resource attributes, distributional variances between celebrities and normal people, and spurious generation. our contributions are summarized as follows: ## problem definition and dataset in this section, we first define the open-domain user profile inference and then describe the dataset collection in detail. ## methods in this section, we discuss our methods for opendomain twitter user profile inference. first, we introduce an extraction-based method that largely follows the principle from @xcite and @xcite . then we discuss our proposed prompt-based generation approach that provides a unified view to infer different attribute values, and can further infer values that do not appear in the twitter context. ## experiments in this section, we conduct experiments on our constructed dataset and user profile extraction dataset @xcite . then we provide a qualitative analysis and discuss the remaining challenges. ## conclusion in this paper, we first explore open-domain twitter user profile inference. we use the combination of wikidata and twitter information to create a largescale dataset. we propose to use a generation-based method with attributes as prompts and compare it with the extraction-based method. the result shows that the generation-based method can significantly outperform the extraction-based method on opendomain profile inference, with the ability to perform both direct extraction and indirect inference. our further analysis still finds some of the errors and remaining challenges of the generation-based method, such as degraded performances for lowresource attributes and spurious generation, which reveals the limits of our current generation-based user profile inference model.