How are the tweets selected?
They collected tweets from US and then applied some filtering rules based on Lexicons
We present the Twitter Job/Employment Corpus, a collection of tweets annotated by a humans-in-the-loop supervised learning framework that integrates crowdsourcing contributions and expertise on the local community and employment environment. Previous computational studies of job-related phenomena have used corpora collected from workplace social media that are hosted internally by the employers, and so lacks independence from latent job-related coercion and the broader context that an open domain, general-purpose medium such as Twitter provides. Our new corpus promises to be a benchmark for the extraction of job-related topics and advanced analysis and modeling, and can potentially benefit a wide range of research communities in the future.
Working American adults spend more than one third of their daily time on job-related activities BIBREF0 —more than on anything else. Any attempt to understand a working individual's experiences, state of mind, or motivations must take into account their life at work. In the extreme, job dissatisfaction poses serious health risks and even leads to suicide BIBREF1 , BIBREF2 . Conversely, behavioral and mental problems greatly affect employee's productivity and loyalty. 70% of US workers are disengaged at work BIBREF3 . Each year lost productivity costs between 450 and 550 billion dollars. Disengaged workers are 87% more likely to leave their jobs than their more satisfied counterparts are BIBREF3 . The deaths by suicide among working age people (25-64 years old) costs more than $44 billion annually BIBREF4 . By contrast, behaviors such as helpfulness, kindness and optimism predict greater job satisfaction and positive or pleasurable engagement at work BIBREF5 . A number of computational social scientists have studied organizational behavior, professional attitudes, working mood and affect BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , but in each case: the data they investigated were collected from internal interactive platforms hosted by the workers' employers. These studies are valuable in their own right, but one evident limitation is that each dataset is limited to depicting a particular company and excludes the populations who have no access to such restricted networks (e.g., people who are not employees of that company). Moreover, the workers may be unwilling to express, e.g., negative feelings about work (“I don't wanna go to work today”), unprofessional behavior (“Got drunk as hell last night and still made it to work”), or a desire to work elsewhere (“I want to go work at Disney World so bad”) on platforms controlled by their employers. A major barrier to studying job-related discourse on general-purpose, public social media—one that the previous studies did not face—is the problem of determining which posts are job-related in the first place. There is no authoritative training data available to model this problem. Since the datasets used in previous work were collected in the workplace during worktime, the content is implicitly job-related. By contrast, the subject matter of public social media is much more diverse. People with various life experiences may have different criteria for what constitutes a “job” and describe their jobs differently. For instance, a tweet like “@SOMEONE @SOMEONE shit manager shit players shit everything” contains the job-related signal word “manager,” yet the presence of “players” ultimately suggests this tweet is talking about a sport team. Another example “@SOMEONE anytime for you boss lol” might seem job-related, but “boss” here could also simply refer to “friend” in an informal and acquainted register. Extracting job-related information from Twitter can be valuable to a range of stakeholders. For example, public health specialists, psychologists and psychiatrists could use such first-hand reportage of work experiences to monitor job-related stress at a community level and provide professional support if necessary. Employers might analyze these data and use it to improve how they manage their businesses. It could help employees to maintain better online reputations for potential job recruiters as well. It is also meaningful to compare job-related tweets against non-job-related discourse to observe and understand the linguistic and behavioral similarities and differences between on- and off-hours. Our main contributions are: