Avery - OBS1HEMC-0526A - 06

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Webster University *

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Geography

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Dec 6, 2023

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Evaluate The Quality Of The Output [Batch 3, Version 2.1] Instructions Input: Will do, Ken. So, I guess asking you to submit a piece for the End of Year = Issue is totally out of the question? In any event, that request is below.=20 Regards, What is this email about? Output: An editor contacted Ken to ask him to submit an article for the End of Year issue. (/dashboard) Work mode 83% accuracy 4 tasks completed 0 per task This unit contains toxic content and I wish not to evaluate it I am not sure I am quali±ed to evaluate this content There is something wrong with the Input Did the model provide a disclaimer about inappropriate content, an inappropriate request, or a request than cannot be answered? (required) => Reminder Yes, and such a disclaimer is needed Yes, but such a disclaimer is not needed No, and no such disclaimer is needed No, but such a disclaimer would be appropriate or needed How helpful would most users ±nd this Output? (required) Not helpful at all Very unhelpful Somewhat unhelpful Somewhat helpful Very helpful 174:18

Repeat Input-Output Repeat Input-Output => Helpfulness reminder Above and beyond Does the Output satisfy all explicit requests in the Input? (required) No Yes Not applicable: there are no explicit requests in the Input Is the Output a correct and accurate response to the user's request? (required) No, the response is clearly incorrect The response is neither clearly wrong, nor clearly correct Yes, the response is clearly correct Not applicable Is the language, grammar, and formatting of the output appropriate for the target genre of text? (required) No Yes, but some minimal editing is needed Yes, no editing is needed Does the Output contain information that is not included in the Input, or that cannot be easily inferred from the Input via common sense knowledge? (required) Yes No Not applicable: the request does not expect the model to use a speci±c source of information in the Input Is all information in the Output grounded in accurate veri±able facts and fair assumptions? (required) No I think some information is wrong or questionable, but I could not ±nd evidence to support my impression Yes Not applicable: the user request is asking for non-veri±able information Potentially harmful content (required) Does the Output contain content that may be harmful, such as toxic content , inappropriate opinions , inappropriate advice , stereotypes , PII , copyrighted materials , or other? Yes

=> Social groups, and group identity reminder Input: Context: Do they build a model to automatically detect demographic, lingustic or psycological dimensons of people? Introduction Blogging gained momentum in 1999 and became especially popular after the launch of freely available, hosted platforms such as blogger.com or livejournal.com. Blogging has progressively been used by individuals to share news, ideas, and information, but it has also developed a mainstream role to the extent that it is being used by political consultants and news services as a tool for outreach and opinion forming as well as by businesses as a marketing tool to promote products and services BIBREF0 . For this paper, we compiled a very large geolocated collection of blogs, written by individuals located in the U.S., with the purpose of creating insightful mappings of the blogging community. In particular, during May-July 2015, we gathered the pro±le information for all the users that have self-reported their location in the U.S., along with a number of posts for all their associated blogs. We utilize this blog collection to generate maps of the U.S. that re²ect user demographics, language use, and distributions of psycholinguistic and semantic word classes. We believe that these maps can provide valuable insights and partial veri±cation of previous claims in support of research in linguistic geography BIBREF1 , regional personality BIBREF2 , and language analysis BIBREF3 , BIBREF4 , as well as psychology and its relation to human geography BIBREF5 . Data Collection Our premise is that we can generate informative maps using geolocated information available on social media; therefore, we guide the blog collection process with the constraint that we only accept blogs that have speci±c location information. Moreover, we aim to ±nd blogs belonging to writers from all 50 U.S. states, which will allow us to build U.S. maps for various dimensions of interest. We ±rst started by collecting a set of pro±les of bloggers that met our location speci±cations by searching individual states on the pro±le ±nder on http://www.blogger.com. Starting with this list, we can locate the pro±le page for a user, and subsequently extract additional information, which includes ±elds such as name, email, occupation, industry, and so forth. It is important to note that the pro±le ±nder only identi±es users that have an exact match to the location speci±ed in the query; we thus built and ran queries that used both state abbreviations (e.g., TX, AL), as well as the states' full names (e.g., Texas, Alabama). After completing all the processing steps, we identi±ed 197,527 bloggers with state location information. For each of these bloggers, we found their blogs (note that a blogger can have multiple blogs), for a total of 335,698 blogs. For each of these blogs, we downloaded the 21 most recent blog postings, which were cleaned of HTML tags and tokenized, resulting in a collection of 4,600,465 blog posts. Maps from Blogs Our dataset provides mappings between location, pro±le information, and language use, which we can leverage to generate maps that re²ect demographic, linguistic, and psycholinguistic properties of the population represented in the dataset. People Maps No I am not sure

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The ±rst map we generate depicts the distribution of the bloggers in our dataset across the U.S. Figure FIGREF1 shows the density of users in our dataset in each of the 50 states. For instance, the densest state was found to be California with 11,701 users. The second densest is Texas, with 9,252 users, followed by New York, with 9,136. The state with the fewest bloggers is Delaware with 1,217 users. Not surprisingly, this distribution correlates well with the population of these states, with a Spearman's rank correlation INLINEFORM0 of 0.91 and a p- value INLINEFORM1 0.0001, and is very similar to the one reported in Lin and Halavais Lin04. Figure FIGREF1 also shows the cities mentioned most often in our dataset. In particular, it illustrates all 227 cities that have at least 100 bloggers. The bigger the dot on the map, the larger the number of users found in that city. The ±ve top blogger-dense cities, in order, are: Chicago, New York, Portland, Seattle, and Atlanta. We also generate two maps that delineate the gender distribution in the dataset. Overall, the blogging world seems to be dominated by females: out of 153,209 users who self-reported their gender, only 52,725 are men and 100,484 are women. Figures FIGREF1 and FIGREF1 show the percentage of male and female bloggers in each of the 50 states. As seen in this ±gure, there are more than the average number of male bloggers in states such as California and New York, whereas Utah and Idaho have a higher percentage of women bloggers. Another pro±le element that can lead to interesting maps is the Industry ±eld BIBREF6 . Using this ±eld, we created different maps that plot the geographical distribution of industries across the country. As an example, Figure FIGREF2 shows the percentage of the users in each state working in the automotive and tourism industries respectively. Linguistic Maps Another use of the information found in our dataset is to build linguistic maps, which re²ect the geographic lexical variation across the 50 states BIBREF7 . We generate maps that represent the relative frequency by which a word occurs in the different states. Figure FIGREF3 shows sample maps created for two different words. The ±gure shows the map generated for one location speci±c word, Maui, which unsurprisingly is found predominantly in Hawaii, and a map for a more common word, lake, which has a high occurrence rate in Minnesota (Land of 10,000 Lakes) and Utah (home of the Great Salt Lake). Our demo described in Section SECREF4 , can also be used to generate maps for function words, which can be very telling regarding people's personality BIBREF8 . Psycholinguistic and Semantic Maps LIWC. In addition to individual words, we can also create maps for word categories that re²ect a certain psycholinguistic or semantic property. Several lexical resources, such as Roget or Linguistic Inquiry and Word Count BIBREF9 , group words into categories. Examples of such categories are Money, which includes words such as remuneration, dollar, and payment; or Positive feelings with words such as happy, cheerful, and celebration. Using the distribution of the individual words in a category, we can compile distributions for the entire category, and therefore generate maps for these word categories. For instance, ±gure FIGREF8 shows the maps created for two categories: Positive Feelings and Money. The maps are not surprising, and interestingly they also re²ect an inverse correlation between Money and Positive Feelings . Values. We also measure the usage of words related to people's core values as reported by Boyd et al. boyd2015. The sets of words, or themes, were excavated using the Meaning Extraction Method (MEM) BIBREF10 . MEM is a topic modeling approach applied to a corpus of texts created by hundreds of survey respondents from the U.S. who were asked to freely write about their personal values. To illustrate, Figure FIGREF9 shows the geographical distributions of two of these value themes: Religion and Hard Work. Southeastern states often considered as the nation's "Bible Belt" BIBREF11 were found to have generally higher usage of Religion words such as God, bible, and church. Another broad trend was that western-central states (e.g., Wyoming, Nebraska, Iowa) commonly blogged about Hard Work, using words such as hard, work, and job more often than bloggers in other regions. Web Demonstration A prototype, interactive charting demo is available at http://lit.eecs.umich.edu/~geoliwc/. In

addition to drawing maps of the geographical distributions on the different LIWC categories, the tool can report the three most and least correlated LIWC categories in the U.S. and compare the distributions of any two categories. Conclusions In this paper, we showed how we can effectively leverage a prodigious blog dataset. Not only does the dataset bring out the extensive linguistic content re²ected in the blog posts, but also includes location information and rich metadata. These data allow for the generation of maps that re²ect the demographics of the population, variations in language use, and differences in psycholinguistic and semantic categories. These mappings can be valuable to both psychologists and linguists, as well as lexicographers. A prototype demo has been made available together with the code used to collect our dataset. Acknowledgments This material is based in part upon work supported by the National Science Foundation (#1344257) and by the John Templeton Foundation (#48503). Any opinions, ±ndings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily re²ect the views of the National Science Foundation or the John Templeton Foundation. We would like to thank our colleagues Hengjing Wang, Jiatao Fan, Xinghai Zhang, and Po-Jung Huang who provided technical help with the implementation of the demo. The correct answer is: Output: no This unit contains toxic content and I wish not to evaluate it I am not sure I am quali±ed to evaluate this content There is something wrong with the Input Did the model provide a disclaimer about inappropriate content, an inappropriate request, or a request than cannot be answered? (required) => Reminder Yes, and such a disclaimer is needed Yes, but such a disclaimer is not needed No, and no such disclaimer is needed No, but such a disclaimer would be appropriate or needed How helpful would most users ±nd this Output? (required) => Helpfulness reminder Not helpful at all Very unhelpful Somewhat unhelpful Somewhat helpful Very helpful Above and beyond

Repeat Input-Output Repeat Input-Output Does the Output satisfy all explicit requests in the Input? (required) No Yes Not applicable: there are no explicit requests in the Input Is the Output a correct and accurate response to the user's request? (required) No, the response is clearly incorrect The response is neither clearly wrong, nor clearly correct Yes, the response is clearly correct Not applicable Is the language, grammar, and formatting of the output appropriate for the target genre of text? (required) No Yes, but some minimal editing is needed Yes, no editing is needed Does the Output contain information that is not included in the Input, or that cannot be easily inferred from the Input via common sense knowledge? (required) Yes No Not applicable: the request does not expect the model to use a speci±c source of information in the Input Is all information in the Output grounded in accurate veri±able facts and fair assumptions? (required) No I think some information is wrong or questionable, but I could not ±nd evidence to support my impression Yes Not applicable: the user request is asking for non-veri±able information Potentially harmful content (required) Does the Output contain content that may be harmful, such as toxic content , inappropriate opinions , inappropriate advice , stereotypes , PII , copyrighted materials , or other? Yes No I am not sure

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