The socioeconomic background of people and how they use standard forms of
language are not independent, as demonstrated in various sociolinguistic
studies. However, the extent to which these correlations may be influenced by
the mixing of people from different socioeconomic classes remains relatively
unexplored from a quantitative perspective. In this work we leverage geotagged
tweets and transferable computational methods to map deviations from standard
English on a large scale, in seven thousand administrative areas of England and
Wales. We combine these data with high-resolution income maps to assign a proxy
socioeconomic indicator to home-located users. Strikingly, across eight
metropolitan areas we find a consistent pattern suggesting that the more
different socioeconomic classes mix, the less interdependent the frequency of
their departures from standard grammar and their income become. Further, we
propose an agent-based model of linguistic variety adoption that sheds light on
the mechanisms that produce the observations seen in the data.

人们的社会经济背景和他们使用标准语言形式相关联，在各种社会语言学研究中已有证明。然而，从定量的角度来看，不同社会经济阶层之间的混合可能会对这些相关性产生影响，而这方面的研究相对较少。本研究利用地理标记的推特和可转移的计算方法，在英格兰和威尔士的七千个行政区域上大规模地绘制非标准英语的偏离，与高分辨率的收入地图结合，为家庭位置用户分配一个代理社会经济指标。令人惊讶的是，在八个大都市区域，我们发现一个一致的模式，即不同社会经济阶层混合得越多，他们离标准语法的频率和收入的相互依赖性越小。此外，我们提出了一个基于代理人的语言多样性采纳模型，对产生数据中所观察到的现象提供了解释。

方言交融：社会经济混合对语言使用的影响

When Dialects Collide: How Socioeconomic Mixing Affects Language Use

Recent studies suggest social media activity can function as a proxy for
measures of state-level public health, detectable through natural language
processing. We present results of our efforts to apply this approach to
estimate homelessness at the state level throughout the US during the period
2010-2019 and 2022 using a dataset of roughly 1 million geotagged tweets
containing the substring ``homeless.'' Correlations between
homelessness-related tweet counts and ranked per capita homelessness volume,
but not general-population densities, suggest a relationship between the
likelihood of Twitter users to personally encounter or observe homelessness in
their everyday lives and their likelihood to communicate about it online. An
increase to the log-odds of ``homeless'' appearing in an English-language
tweet, as well as an acceleration in the increase in average tweet sentiment,
suggest that tweets about homelessness are also affected by trends at the
nation-scale. Additionally, changes to the lexical content of tweets over time
suggest that reversals to the polarity of national or state-level trends may be
detectable through an increase in political or service-sector language over the
semantics of charity or direct appeals. An analysis of user account type also
revealed changes to Twitter-use patterns by accounts authored by individuals
versus entities that may provide an additional signal to confirm changes to
homelessness density in a given jurisdiction. While a computational approach to
social media analysis may provide a low-cost, real-time dataset rich with
information about nationwide and localized impacts of homelessness and
homelessness policy, we find that practical issues abound, limiting the
potential of social media as a proxy to complement other measures of
homelessness.

研究发现，社交媒体的活动可以作为状态级公共卫生测量的代理，可通过自然语言处理进行检测。该研究应用该方法来估计美国各州在 2010-2019 年和 2022 年期间的无家可归者人数，并使用包含 “无家可归者” 子字符串的大约 100 万个地理标记推文数据集。

通过代理社交媒体信号评估测量本地无家可归者水平

An assessment of measuring local levels of homelessness through proxy  social media signals

In this paper, we consider the problem of predicting demographics of
geographic units given geotagged Tweets that are composed within these units.
Traditional survey methods that offer demographics estimates are usually
limited in terms of geographic resolution, geographic boundaries, and time
intervals. Thus, it would be highly useful to develop computational methods
that can complement traditional survey methods by offering demographics
estimates at finer geographic resolutions, with flexible geographic boundaries
(i.e. not confined to administrative boundaries), and at different time
intervals. While prior work has focused on predicting demographics and health
statistics at relatively coarse geographic resolutions such as the county-level
or state-level, we introduce an approach to predict demographics at finer
geographic resolutions such as the blockgroup-level. For the task of predicting
gender and race/ethnicity counts at the blockgroup-level, an approach adapted
from prior work to our problem achieves an average correlation of 0.389
(gender) and 0.569 (race) on a held-out test dataset. Our approach outperforms
this prior approach with an average correlation of 0.671 (gender) and 0.692
(race).

提出了一种基于地理位置标记推特的计算方法，能够以细微的地理分辨率、灵活的地理边界与不同时间间隔推测人口统计学数据，通过对性别、种族 / 族裔等因素的预测，超越了传统方法的平均相关性。