#petpeeve: Annoying Behaviors Dataset

A #petpeeve (annoying behaviors) dataset of 3,375 tweets labeled with annoying behavior categories, collected by Wang and Yang via Twitter API (2015). Each record contains tweet text and annoying behavior category label. The dataset is notable for its dataset for automatic categorization of annoying behaviors using lexical and frame-semantic embeddings. Intended for research into social media analysis, text classification, and behavioral categorization.

#Frame semantics#Hashtag#Petpeeve

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Analytics

5.7
Flesch–Kincaid grade
US school grade level
8.6
Automated readability index
Frequent terms
petpeevepeopleppllikedonlolshitasspetpeevesgirlsknowniggaswithatesmhniggabitchescontbitchgottalklmaothinkwannadamndatdontfucktwitterwenbadmanhairgirlwtfaintaskphonetrywantlooktimechicksuglyfemalesmentellrealstopword
Topic clusters

Topic 0

girl
petpeeve
bitch
chick
ass
shoe
wear
woman

Topic 1

niggas
nigga
petpeeve
gucci
wit
ass
bitch
shit

Topic 2

petpeeve
car
drive
stop
lifestyle
drink
train
light

Topic 3

people
petpeeve
ppl
ask
know
think
like
shit

Topic 4

lol
petpeeve
funny
time
tell
movie
wait
like

Topic 5

phone
text
bbm
word
petpeeve
reply
txt
respond

Topic 6

twitter
tweet
petpeeve
follow
ppl
people
follower
shit

Topic 7

hate
people
lie
petpeeve
ppl
liar
liars
hater
Top hashtags
#petpeeve3.1K
#petpeeves197
#fail13
#cmonson8
#random7
#imjustsayin6
#petpeevee6
#turnoff6
#twitterjail6
#comeonson5
#foh5
#fuckouttahere5
#killyaself5
#mypetpeeve5
#stopit55
#tt5
#whatsworsethan5
#wtf5
#blameitonitaly4
#dead4

Computed over the full corpus.

Record

Catalog key
petpeeve
Authors
Wang, William Yang; Yang, Diyi
Year
2015
Source paper
That's So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets
DOI / arXiv
10.18653/v1/D15-1306
License
Academic use (see UCSB) (LicenseRef-Research-Only)
Records
3,375
Text corpus
tweet text
Languages
en
Collected
2015 (2010s)
Region
Global (inferred: the platform is used worldwide)

Provenance

Originally published at
https://sites.cs.ucsb.edu/~william/software.html link checked
Hosted copy
Mirrored on echo.directory on 2026-10-09, 323.8 KB across 3 files. Checksums in MANIFEST.json.

Citations

That’s So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets
W. Y. Wang, Diyi Yang
Journal Article · Conference on Empirical Methods in Natural Language Processing · 2015

321
citations
well cited
17
influential citations
5% of all citations

Cited by

Show all 10

Citation data from Semantic Scholar, retrieved 2026-10-19.

How to cite

BibTeX

@misc{echo2026,
  title  = {ECHO: Exploratory Compendium of Humanity Online},
  author = {Switzer, Braden and Zafarani, Reza},
  year   = {2026},
  url    = {https://echo.directory},
  note   = {Syracuse University}
}

@inproceedings{wang2015annoying,
  title     = {That's So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets},
  author    = {Wang, William Yang and Yang, Diyi},
  booktitle = {EMNLP},
  year      = {2015}
}

APA

Switzer, B., & Zafarani, R. (2026). ECHO: Exploratory Compendium of Humanity Online [Data repository]. Syracuse University. https://echo.directory

Wang, W. Y., & Yang, D. (2015). #petpeeve: Annoying Behaviors Dataset [Data set]. https://doi.org/10.18653/v1/D15-1306

MLA

Switzer, Braden, and Reza Zafarani. ECHO: Exploratory Compendium of Humanity Online. Syracuse University, 2026, echo.directory.

Wang, William Yang, and Diyi Yang. #petpeeve: Annoying Behaviors Dataset. 2015, doi.org/10.18653/v1/D15-1306.

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