NeoCov

Semantic change and socio-semantic variation. The case of Covid-related neologisms on Reddit.

Description

This repository contains the code for the paper Semantic change and socio-semantic variation. The case of Covid-related neologisms on Reddit. This paper has been submitted and is currently under (anonymous) review for the journal Linguistics Vanguard.

You can clone the repository and install the code as a Python package named neocov by running pip install . within the cloned directory. This will automatically install all dependencies. As always, it is recommended to install this package in a virtual environment (e.g. using conda).

The Reddit data used for this paper are too big to make them available here. Some parts of the code cannot be executed without having access to these datasets. The full datasets of Reddit comments and the models trained from these comments can be requested via email once the anonymous review process is finished. The datasets and models allow to reproduce our results.

This repository provides the code used to process the Reddit comments, train the models, and produce the results presented in our paper. The code was written and documented using the literate programming framework nbdev and the documentation is available here:

https://wuqui.github.io/neocov/

The code used for the tables and figures contained in the paper can be found directly via the following links:

Reference	Link
Table 2	semantic neologisms
Figure 1	Covid-related communities
Figure 2	Semantic axes
Figure 3	Semantic maps for vaccines

Imports

# all_data

%load_ext autoreload
%autoreload 2

from neocov.read_data import *
from neocov.preproc import *
from neocov.type_emb import *
from neocov.communities import *

from pathlib import Path
import pandas as pd
pd.set_option('display.max_rows', 100)
import altair as alt
from altair_saver import save
from gensim.models import Word2Vec
import pickle

Variables

DATA_DIR = '../data/'
COMMENTS_DIAC_DIR = f'{DATA_DIR}comments/by_date/'
COMMENTS_DIR_SUBR = f'{DATA_DIR}comments/subr/'
OUT_DIR = '../out/'

Detecting semantic change

Read comments

YEAR = '2020'

comment_paths_year = get_comments_paths_year(COMMENTS_DIAC_DIR, YEAR)

comments = read_comm_csvs(comment_paths_year)

comments

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	author	body	created_utc	id	subreddit
0	Broncos57	Oh okay thank you so much for the reply! I rea...	2020-04-14 21:20:57	fnf0nqd	boston
1	tresclow	Es tan deprimente ver cuando esta clase de est...	2020-04-14 21:20:57	fnf0noq	chile
2	Hicklebear	This comment is Codex approved.	2020-04-14 21:20:57	fnf0nor	Grimdank
3	[deleted]	[removed]	2020-04-14 21:20:57	fnf0nos	acturnips
4	ilovedog5	Am I the only person who thinks this whole thi...	2020-04-14 21:20:57	fnf0not	UnresolvedMysteries
...	...	...	...	...	...
9599965	Driedrain	Invoice sent!	2020-08-19 21:59:59	g25e6cy	hardwareswap
9599966	PresOfTheLesbianClub	Yes. Fixed that. Thank you!	2020-08-19 21:59:59	g25e6cz	vanderpumprules
9599967	originalasteele	This is incredible!! Oh, how I miss Midna	2020-08-19 21:59:59	g25e6d1	zelda
9599968	sunbeam2z	I boosted you. I don't need a boost.	2020-08-19 21:59:59	g25e6ci	Earnin
9599969	Unicornius	Yeah its weird, I think Jamal has passed to hi...	2020-08-19 21:59:59	g25e6bf	denvernuggets

9599970 rows × 5 columns

Preprocessing

comments_clean = clean_comments(comments)

conv_to_lowerc       (9599970, 5) 0:00:08.393617      
rm_punct             (9599970, 5) 0:00:57.095767      
tokenize             (9599970, 5) 0:04:38.659268      
rem_short_comments   (5125011, 5) 0:01:27.863816

Dataset of comments after pre-processing:

comments_clean.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 5125011 entries, 0 to 9599969
Data columns (total 5 columns):
 #   Column       Dtype         
---  ------       -----         
 0   author       string        
 1   body         object        
 2   created_utc  datetime64[ns]
 3   id           string        
 4   subreddit    string        
dtypes: datetime64[ns](1), object(1), string(3)
memory usage: 234.6+ MB

docs = comments_clean['body'].to_list()

Saving the cleaned comments to disk:

with open(f'{OUT_DIR}docs_clean/diac_{YEAR}.pickle', 'wb') as fp:
    pickle.dump(docs, fp)

Loading the cleaned comments from disk:

with open(f'{OUT_DIR}docs_clean/diac_{YEAR}.pickle', 'rb') as fp:
    docs = pickle.load(fp)

Train models

Create corpus

corpus = Corpus(docs)

Train model

%%time
model = train_model(corpus, EPOCHS=20)

len(model.wv.key_to_index)

Save model

model.save(f'{OUT_DIR}models/{YEAR}_ep-20.model')

Load models

model_2019 = Word2Vec.load(f'{OUT_DIR}models/2019.model')

model_2020 = Word2Vec.load(f'{OUT_DIR}models/2020.model')

Align models

model_2019_vocab = len(model_2019.wv.key_to_index)
model_2020_vocab = len(model_2020.wv.key_to_index)

smart_procrustes_align_gensim(model_2019, model_2020)

190756 190756
190756 190756





<gensim.models.word2vec.Word2Vec at 0x1738f1030>

assert len(model_2019.wv.key_to_index) == len(model_2020.wv.vectors)

Overview of vocabulary sizes for both models before Procrustes alignment:

models_vocab = pd.DataFrame(
    columns=['Model', 'Words'],
    data=[
        ['2019', model_2019_vocab],
        ['2020', model_2020_vocab],
        ['intersection', len(model_2019.wv.key_to_index)]
    ],
)

models_vocab

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	Model	Words
0	2019	252564
1	2020	277707
2	intersection	190756

models_vocab.to_csv(f'{OUT_DIR}models_vocab.csv', index=False)

Measure distances

Measuring semantic distances (~ cosine distance) between the 2019 and the 2020 model for all words contained in the aligned vocabulary.

distances = measure_distances(model_2019, model_2020)

20 words that show the highest semantic distance between 2019 and 2020. This output is presented in Table 2 in the paper.

blacklist_lex = load_blacklist_lex()

k = 20
freq_min = 100

sem_change_cands = (distances\
    .query('freq_1 > @freq_min and freq_2 > @freq_min')
    .query('lex.str.isalpha() == True')
    .query('lex.str.len() > 3')
    .query('lex not in @blacklist_lex')
    .nlargest(k, 'dist_sem')
    .reset_index(drop=True)
)

sem_change_cands

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	lex	dist_sem	freq_1	freq_2
0	lockdowns	1.016951	940	990
1	maskless	0.996101	118	127
2	sunsetting	0.996084	111	119
3	childe	0.980564	209	221
4	megalodon	0.975273	751	792
5	newf	0.962381	107	115
6	corona	0.926739	3553	3684
7	filtrate	0.918609	102	110
8	chaz	0.899856	190	202
9	klee	0.888728	161	173
10	rona	0.886363	409	433
11	cerb	0.869179	315	333
12	rittenhouse	0.866206	181	194
13	vacuo	0.862142	170	181
14	moderna	0.843465	199	211
15	pandemic	0.837175	9504	9957
16	spreader	0.835257	164	175
17	distancing	0.833737	2910	3038
18	sars	0.827039	880	924
19	quarantines	0.820280	149	160

Output semantic neologisms for inclusion in the paper.

sem_change_cands_out = (sem_change_cands
    .nlargest(100, 'dist_sem')
    .assign(index_1 = lambda df: df.index + 1)
    .assign(dist_sem = lambda df: df['dist_sem'].round(2))
    .assign(dist_sem = lambda df: df['dist_sem'].apply('{:.2f}'.format))
    .rename({'index_1': '', 'lex': 'Lexeme', 'dist_sem': 'SemDist'}, axis=1)
)

sem_change_cands_out.to_csv(
        f'{OUT_DIR}sem_change_cands.csv',
        columns=['', 'Lexeme', 'SemDist'],
        index=False
    )

Inspect neighbourhood

Closer inspection of semantic neighbours between 2019 and 2020 for the term distancing. Unfortunately, due to the space limitation, these results had to be excluded from the paper.

LEX_NBS = 'distancing'

nbs_model_1, nbs_model_2 = get_nearest_neighbours_models(
    lex=LEX_NBS, 
    freq_min=25,
    model_1=model_2019, 
    model_2=model_2020,
    k=10
)

display(
    nbs_model_1,
    nbs_model_2
)

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	Model	Word	SemDist	Freq
0	1	distanced	0.22	309
1	1	extricate	0.27	32
2	1	detaching	0.34	61
3	1	disassociate	0.34	93
4	1	offing	0.36	48
5	1	recuse	0.38	50
6	1	recused	0.40	29
7	1	isolating	0.42	685
8	1	detach	0.44	245
9	1	distract	0.45	1553

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	Model	Word	SemDist	Freq
50601	2	distanced	0.46	326
50602	2	isolation	0.46	2037
50603	2	gatherings	0.47	921
50604	2	distance	0.48	11355
50605	2	lockdowns	0.50	990
50606	2	quarantine	0.53	5225
50607	2	masks	0.53	8997
50608	2	quarantining	0.53	279
50609	2	quarantines	0.53	160
50610	2	lockdown	0.53	4642

(nbs_model_1.filter(['Word', 'SemDist'])
	.to_csv(f'{OUT_DIR}nbs_{LEX_NBS}_2019.csv', float_format='%.2f', index=False))

(nbs_model_2.filter(['Word', 'SemDist'])
	.to_csv(f'{OUT_DIR}nbs_{LEX_NBS}_2020.csv', float_format='%.2f', index=False))

Social semantic variation

Covid-related communities

In this section, we determine those communities which are most actively engaged in Covid-related discourse.

read comments

comments_dir_path = Path('../data/comments/lexeme/')
comments_paths = list(comments_dir_path.glob(f'Covid*.csv'))

%%time
comments = read_comm_csvs(comments_paths)
comments

CPU times: user 36.6 s, sys: 11.4 s, total: 48 s
Wall time: 59 s

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	author	body	created_utc	id	subreddit
0	Gloob_Patrol	I assume you work too so he's feeling like he ...	2020-09-08 18:53:06	g4guhl5	LongDistance
1	amtrusc	Strep swab and culture negative, I’m sure? Cou...	2020-09-08 18:53:08	g4guhsm	tonsilstones
2	Ephuntz	>Good point. My apologies. It's just becomi...	2020-09-08 18:53:09	g4guhua	Winnipeg
3	cstransfer	Have you noticed an increase of people going e...	2020-09-08 18:53:09	g4guhu4	financialindependence
4	IlliniWhoDat	I haven't. I have seen it online, but haven't...	2020-09-08 18:53:13	g4gui6o	KoreanBeauty
...	...	...	...	...	...
3800760	willw	Last group pre COVID!	2020-07-01 21:59:48	fwmqfbj	jawsurgery
3800761	Daikataro	If everyone is infected with COVID, new cases ...	2020-07-01 21:59:49	fwmqff2	politics
3800762	StabYourBloodIntoMe	> If the mortality rate is actually decreas...	2020-07-01 21:59:50	fwmqfib	dataisbeautiful
3800763	Shorse_rider	I was a freelancer until covid and earned more...	2020-07-01 21:59:55	fwmqfuw	AskWomen
3800764	Gayfetus	This is actually fascinating and possibly incr...	2020-07-01 21:59:57	fwmqfz0	Coronavirus

3800765 rows × 5 columns

get subreddit counts

subr_counts = get_subr_counts(comments)

Top 15 communities that are most actively engaged in Covid-related discourse.

subr_counts_plt = plot_subr_counts(subr_counts, k=15)
subr_counts_plt

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subr_counts_plt.save(f'{OUT_DIR}subr_counts.png', scale_factor=2.0)

Train models

In this section, we train community-specific embedding models.

SUBR = 'Coronavirus'

fpaths = get_comments_paths_subr(COMMENTS_DIR_SUBR, SUBR)
comments = read_comm_csvs(fpaths)

%%time
comments_clean = clean_comments(comments)

conv_to_lowerc       (4121144, 5) 0:00:08.279838      
rm_punct             (4121144, 5) 0:00:31.917256      
tokenize             (4121144, 5) 0:07:40.929735      
rem_short_comments   (2927221, 5) 0:01:04.440039      
CPU times: user 1min 21s, sys: 3min 17s, total: 4min 38s
Wall time: 10min 42s

docs = comments_clean['body']
docs = docs.to_list()

with open(f'{OUT_DIR}docs_clean/{SUBR}.pickle', 'wb') as fp:
    pickle.dump(docs, fp)

Load pre-processed comments from disk.

with open(f'{OUT_DIR}docs_clean/{SUBR}.pickle', 'rb') as fp:
    docs = pickle.load(fp)

f'{len(docs):,}'

'2,927,221'

corpus = Corpus(docs)

%%time
model = train_model(corpus, EPOCHS=20)

CPU times: user 21min 15s, sys: 10.7 s, total: 21min 26s
Wall time: 4min 44s

Print vocabulary size.

f'{len(model.wv.key_to_index):,}'

'38,558'

model.save(f'{OUT_DIR}models/{SUBR}.model')

Load models

model_names = ['Coronavirus', 'conspiracy']

models = []
for name in model_names:
	model = make_model_dict(name)
	model['model'] = Word2Vec.load(model['path'])
	models.append(model)

Align models

for model in models:
	model['vocab'] = len(model['model'].wv.key_to_index)

smart_procrustes_align_gensim(models[0]['model'], models[1]['model'])

67181 67181
67181 67181





<gensim.models.word2vec.Word2Vec at 0x172c82530>

assert len(models[0]['model'].wv.key_to_index) == len(models[1]['model'].wv.key_to_index)

models_vocab = (pd.DataFrame(models)
	.filter(['name', 'vocab'])
	.rename({'name': 'Model', 'vocab': 'Words'}, axis=1)
)

models_vocab

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	Model	Words
0	Coronavirus	94816
1	conspiracy	112599

# models_vocab.to_csv(f'../out/vocabs/vocab_{models[0]["name"]}--{models[1]["name"]}.csv', index=False)

Semantic neighbourhoods

distances = measure_distances(models[0]['model'], models[1]['model'])

words that differ the most between both communities

Due to space limitations, the following results had to be excluded from the paper.

blacklist_lex = load_blacklist_lex()

k = 20
freq_min = 100

sem_change_cands = (distances\
    .query('freq_1 > @freq_min and freq_2 > @freq_min')
    .query('lex.str.isalpha() == True')
    .query('lex.str.len() > 3')
    .query('lex not in @blacklist_lex')
    .nlargest(k, 'dist_sem')
    .reset_index(drop=True)
)

sem_change_cands

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	lex	dist_sem	freq_1	freq_2
0	soliciting	1.003643	2233	2474
1	nimrod	0.974182	103	144
2	incivility	0.958038	16347	15690
3	globes	0.955581	140	193
4	submitter	0.952117	261	352
5	acorn	0.950665	105	148
6	subsequently	0.946088	12174	11956
7	resubmit	0.937007	1763	1927
8	mouthy	0.934621	129	178
9	narc	0.930710	224	305
10	spook	0.920622	317	423
11	nicaraguan	0.914530	111	154
12	yadda	0.913075	184	252
13	amputatorbot	0.905838	9444	9450
14	anons	0.901062	259	351
15	mcnamara	0.899120	108	150
16	editorialization	0.895667	1182	1362
17	spooks	0.890019	263	354
18	durham	0.889415	490	610
19	nist	0.889119	204	277

sem_change_cands_out = (sem_change_cands
    .nlargest(100, 'dist_sem')
    .assign(index_1 = lambda df: df.index + 1)
    .assign(dist_sem = lambda df: df['dist_sem'].round(2))
    .assign(dist_sem = lambda df: df['dist_sem'].apply('{:.2f}'.format))
    .rename({'index_1': '', 'lex': 'Lexeme', 'dist_sem': 'SemDist'}, axis=1)
    .filter(['Lexeme', 'SemDist'])
)
sem_change_cands_out

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	Lexeme	SemDist
0	soliciting	1.00
1	nimrod	0.97
2	incivility	0.96
3	globes	0.96
4	submitter	0.95
5	acorn	0.95
6	subsequently	0.95
7	resubmit	0.94
8	mouthy	0.93
9	narc	0.93
10	spook	0.92
11	nicaraguan	0.91
12	yadda	0.91
13	amputatorbot	0.91
14	anons	0.90
15	mcnamara	0.90
16	editorialization	0.90
17	spooks	0.89
18	durham	0.89
19	nist	0.89

sem_change_cands_out.to_csv(
        f'{OUT_DIR}sem_var_soc_cands.csv',
        index=False
    )

nearest neighbours for target lexemes in both communities

LEX_NBS = 'vaccines'

nbs_model_1, nbs_model_2 = get_nearest_neighbours_models(
    lex=LEX_NBS, 
    freq_min=100,
    model_1=models[0]['model'], 
    model_2=models[1]['model'],
    k=10
)

display(
    nbs_model_1,
    nbs_model_2
)

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	Model	Word	SemDist	Freq	vec
0	1	vaccine	0.25	109094	[1.6486416, -0.77526903, -0.25844133, -2.18959...
1	1	vaccinations	0.31	3305	[1.6486416, -0.77526903, -0.25844133, -2.18959...
2	1	antivirals	0.38	357	[1.6486416, -0.77526903, -0.25844133, -2.18959...
3	1	treatments	0.40	4737	[1.6486416, -0.77526903, -0.25844133, -2.18959...
4	1	drugs	0.41	13655	[1.6486416, -0.77526903, -0.25844133, -2.18959...
5	1	therapies	0.42	425	[1.6486416, -0.77526903, -0.25844133, -2.18959...
6	1	doses	0.43	6558	[1.6486416, -0.77526903, -0.25844133, -2.18959...
7	1	trials	0.45	10095	[1.6486416, -0.77526903, -0.25844133, -2.18959...
8	1	strains	0.46	3875	[1.6486416, -0.77526903, -0.25844133, -2.18959...
9	1	therapeutics	0.46	385	[1.6486416, -0.77526903, -0.25844133, -2.18959...

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	Model	Word	SemDist	Freq	vec
21542	2	vaccinations	0.20	3624	[0.34145182, -1.5721449, -0.045296144, -1.6733...
21543	2	vaccine	0.23	112485	[0.34145182, -1.5721449, -0.045296144, -1.6733...
21544	2	vaccination	0.36	7780	[0.34145182, -1.5721449, -0.045296144, -1.6733...
21545	2	treatments	0.38	4874	[0.34145182, -1.5721449, -0.045296144, -1.6733...
21546	2	medications	0.40	1614	[0.34145182, -1.5721449, -0.045296144, -1.6733...
21547	2	vax	0.42	3208	[0.34145182, -1.5721449, -0.045296144, -1.6733...
21548	2	injections	0.43	795	[0.34145182, -1.5721449, -0.045296144, -1.6733...
21549	2	adjuvants	0.43	199	[0.34145182, -1.5721449, -0.045296144, -1.6733...
21550	2	medicines	0.43	1208	[0.34145182, -1.5721449, -0.045296144, -1.6733...
21551	2	viruses	0.45	17105	[0.34145182, -1.5721449, -0.045296144, -1.6733...

biggest discrepancies in nearest neighbours for target lexemes

lex = 'vaccines'
topn = 15

nbs_model_1, nbs_model_2 = get_nearest_neighbours_models(
    lex=lex, 
    freq_min=100,
    model_1=models[0]['model'], 
    model_2=models[1]['model'],
    k=100_000
)

nbs_diffs = pd.merge(
    nbs_model_1, nbs_model_2, 
    on='Word',
    suffixes = ('_1', '_2')
)

nbs_diffs = nbs_diffs\
    .assign(sim_diff = abs(nbs_diffs['SemDist_1'] - nbs_diffs['SemDist_2']))\
    .sort_values('sim_diff', ascending=True)\
    .reset_index(drop=True)\
    .query('Word.str.len() >= 4')

subr_1_nbs = nbs_diffs\
    .query('SemDist_1 < SemDist_2')\
    .nlargest(topn, 'sim_diff')

subr_2_nbs = nbs_diffs\
    .query('SemDist_2 < SemDist_1')\
    .nlargest(topn, 'sim_diff')

display(subr_1_nbs, subr_2_nbs)

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	Model_1	Word	SemDist_1	Freq_1	vec_1	Model_2	SemDist_2	Freq_2	vec_2	sim_diff
21540	1	candidates	0.48	4842	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.82	4925	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.34
21539	1	dyson	0.77	114	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	1.10	158	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.33
21536	1	parallel	0.80	943	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	1.09	1095	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.29
21530	1	lamp	0.86	224	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	1.14	305	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.28
21531	1	underworld	0.86	115	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	1.14	159	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.28
21526	1	oxford	0.64	4128	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.91	4378	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.27
21519	1	slices	0.81	113	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	1.07	157	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.26
21509	1	fade	0.87	490	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	1.12	611	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.25
21504	1	sputnik	0.68	279	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.93	376	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.25
21507	1	approved	0.64	7276	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.89	7443	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.25
21508	1	preprints	0.63	177	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.88	242	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.25
21484	1	candidate	0.71	8515	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.95	8391	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.24
21485	1	operative	0.97	542	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	1.21	670	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.24
21486	1	designation	0.80	191	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	1.04	261	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.24
21489	1	russians	0.70	2919	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.94	3184	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.24

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	Model_1	Word	SemDist_1	Freq_1	vec_1	Model_2	SemDist_2	Freq_2	vec_2	sim_diff
21541	1	gmos	0.85	130	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.49	179	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.36
21537	1	mandated	1.09	2456	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.79	2732	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.30
21538	1	disrespecting	1.25	231	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.95	314	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.30
21534	1	neuralink	0.98	210	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.69	285	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.29
21535	1	vaxx	0.86	633	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.57	771	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.29
21532	1	poisons	0.84	171	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.56	234	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.28
21524	1	preventable	1.02	1550	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.75	1726	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.27
21525	1	mandating	1.08	840	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.81	998	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.27
21527	1	sugar	0.99	2478	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.72	2747	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.27
21528	1	leukemia	0.90	140	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.63	193	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.27
21522	1	eugenicist	1.11	195	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.85	265	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.26
21523	1	cidrap	1.08	330	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.82	437	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.26
21511	1	mandatory	1.01	9592	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.75	9516	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.26
21512	1	lice	1.00	136	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.74	187	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.26
21513	1	cv19	0.99	539	[1.6486416, -0.77526903, -0.25844133, -2.18959...	2	0.73	667	[0.34145182, -1.5721449, -0.045296144, -1.6733...	0.26

Maps of social semantic variation

The following section contains the plots for Figure 3.

lex = 'vaccines'

nbs_vecs = pd.concat([get_nbs_vecs(lex, model, k=750) for model in models])

common neighbours

#data
nbs_vecs = dim_red_nbs_vecs(nbs_vecs, perplexity=0)

/Users/quirin/opt/miniconda3/envs/neocov/lib/python3.10/site-packages/sklearn/manifold/_t_sne.py:982: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence.
  warnings.warn(

#data
nbs_sim = (nbs_vecs
	.groupby('subreddit')
	.apply(lambda df: df.nlargest(10, 'sim'))
	.reset_index(drop=True)
)

#data
chart_sims = (alt.Chart(nbs_sim).mark_text().encode(
		x='x_tsne:Q',
		y='y_tsne:Q',
		text='lex',
		color='subreddit:N'
	))

chart_sims

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if(typeof define === "function" && define.amd) {
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chart_sims.save(f'../out/map-sem-space_{lex}_sims.pdf')
chart_sims.save(f'../out/map-sem-space_{lex}_sims.html')

differences in neighbours

nbs_vecs = dim_red_nbs_vecs(nbs_vecs, perplexity=70)

/Users/quirin/opt/miniconda3/envs/neocov/lib/python3.10/site-packages/sklearn/manifold/_t_sne.py:982: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence.
  warnings.warn(

nbs_diff = nbs_vecs.drop_duplicates(subset='lex', keep=False)
nbs_diff = (nbs_diff
	.groupby('subreddit')
	.apply(lambda df: df.nlargest(20, 'sim'))
	.reset_index(drop=True)
)

chart_diffs = (alt.Chart(nbs_diff).mark_text().encode(
		x='x_tsne:Q',
		y='y_tsne:Q',
		text='lex:N',
		color='subreddit:N',
		# column='subr_nb:N',
	)).interactive()


chart_diffs

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if(typeof define === "function" && define.amd) {
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chart_diffs.save(f'../out/map-sem-space_{lex}_diffs.pdf')
chart_diffs.save(f'../out/map-sem-space_{lex}_diffs.html')

Dimensions of social semantic variation

The following section presents the plots for Figure 2.

lexs = [ 'corona', 'rona', 'moderna', 'sars', 'spreader', 'maskless', 'distancing', 'quarantines', 'pandemic', 'science', 'research', 'masks', 'lockdowns', 'vaccines' ]

good vs bad

pole_words = ['good', 'bad']

proj_sims = get_axis_sims(lexs, models, pole_words, k=10)

proj_sims = aggregate_proj_sims(proj_sims)

proj_sims_chart = plot_sem_axis(proj_sims, models)
proj_sims_chart

function maybeLoadScript(lib, version) {
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      s.src = paths[lib];
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}

function showError(err) {
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  throw err;
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function displayChart(vegaEmbed) {
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    .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));
}

if(typeof define === "function" && define.amd) {
  requirejs.config({paths});
  require(["vega-embed"], displayChart, err => showError(`Error loading script: ${err.message}`));
} else {
  maybeLoadScript("vega", "5")
    .then(() => maybeLoadScript("vega-lite", "4.17.0"))
    .then(() => maybeLoadScript("vega-embed", "6"))
    .catch(showError)
    .then(() => displayChart(vegaEmbed));
}

})({"config": {"view": {"continuousWidth": 400, "continuousHeight": 300}}, "layer": [{"mark": {"type": "line", "color": "red", "point": {"color": "red"}}, "encoding": {"x": {"field": "conspiracy", "title": "r/conspiracy: red", "type": "quantitative"}, "y": {"field": "lex", "sort": {"field": "SimDiff"}, "title": "", "type": "nominal"}}}, {"mark": {"type": "line", "color": "blue", "point": {"color": "blue"}}, "encoding": {"x": {"field": "Coronavirus", "title": "r/Coronavirus: blue", "type": "quantitative"}, "y": {"field": "lex", "sort": {"field": "SimDiff"}, "title": "", "type": "nominal"}}}], "data": {"name": "data-244f2029e4181be624a777e22fbcc6d3"}, "title": "", "$schema": "https://vega.github.io/schema/vega-lite/v4.17.0.json", "datasets": {"data-244f2029e4181be624a777e22fbcc6d3": [{"lex": "masks", "Coronavirus": 0.07336952537298203, "conspiracy": -0.07284242659807205, "SimDiff": 0.14621195197105408}, {"lex": "vaccines", "Coronavirus": 0.08083311468362808, "conspiracy": -0.03164307773113251, "SimDiff": 0.11247619241476059}, {"lex": "distancing", "Coronavirus": 0.050652213394641876, "conspiracy": -0.0580480694770813, "SimDiff": 0.10870028287172318}, {"lex": "quarantines", "Coronavirus": 0.0307206679135561, "conspiracy": -0.06277474761009216, "SimDiff": 0.09349541552364826}, {"lex": "research", "Coronavirus": 0.12641744315624237, "conspiracy": 0.03304028511047363, "SimDiff": 0.09337715804576874}, {"lex": "lockdowns", "Coronavirus": 0.023748569190502167, "conspiracy": -0.06043205037713051, "SimDiff": 0.08418061956763268}, {"lex": "rona", "Coronavirus": -0.014205201528966427, "conspiracy": -0.08521801233291626, "SimDiff": 0.07101281080394983}, {"lex": "science", "Coronavirus": -0.008252025581896305, "conspiracy": -0.047907549887895584, "SimDiff": 0.03965552430599928}, {"lex": "pandemic", "Coronavirus": -0.02237873524427414, "conspiracy": -0.05953046306967735, "SimDiff": 0.037151727825403214}, {"lex": "moderna", "Coronavirus": 0.08282074332237244, "conspiracy": 0.050235699862241745, "SimDiff": 0.03258504346013069}, {"lex": "maskless", "Coronavirus": -0.031929414719343185, "conspiracy": -0.06295277178287506, "SimDiff": 0.031023357063531876}, {"lex": "spreader", "Coronavirus": -0.14250540733337402, "conspiracy": -0.12802410125732422, "SimDiff": -0.014481306076049805}, {"lex": "sars", "Coronavirus": -0.07047674059867859, "conspiracy": -0.05521240830421448, "SimDiff": -0.015264332294464111}, {"lex": "corona", "Coronavirus": -0.16207775473594666, "conspiracy": -0.1439971923828125, "SimDiff": -0.018080562353134155}]}}, {"mode": "vega-lite"}); </script>

# proj_sims_chart.save(f'../out/proj-emb_{models[0]["name"]}--{models[1]["name"]}___{pole_words[0]}--{pole_words[1]}.pdf')

objective vs subjective

pole_words = ['objective', 'subjective']

proj_sims = get_axis_sims(lexs, models, pole_words, k=10)

proj_sims = aggregate_proj_sims(proj_sims)

proj_sims_chart = plot_sem_axis(proj_sims, models)
proj_sims_chart

function maybeLoadScript(lib, version) {
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      s.async = true;
      s.onload = () => {
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        return resolve(paths[lib]);
      };
      s.onerror = () => reject(`Error loading script: ${paths[lib]}`);
      s.src = paths[lib];
    });
}

function showError(err) {
  outputDiv.innerHTML = `<div class="error" style="color:red;">${err}</div>`;
  throw err;
}

function displayChart(vegaEmbed) {
  vegaEmbed(outputDiv, spec, embedOpt)
    .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));
}

if(typeof define === "function" && define.amd) {
  requirejs.config({paths});
  require(["vega-embed"], displayChart, err => showError(`Error loading script: ${err.message}`));
} else {
  maybeLoadScript("vega", "5")
    .then(() => maybeLoadScript("vega-lite", "4.17.0"))
    .then(() => maybeLoadScript("vega-embed", "6"))
    .catch(showError)
    .then(() => displayChart(vegaEmbed));
}

})({"config": {"view": {"continuousWidth": 400, "continuousHeight": 300}}, "layer": [{"mark": {"type": "line", "color": "red", "point": {"color": "red"}}, "encoding": {"x": {"field": "conspiracy", "title": "r/conspiracy: red", "type": "quantitative"}, "y": {"field": "lex", "sort": {"field": "SimDiff"}, "title": "", "type": "nominal"}}}, {"mark": {"type": "line", "color": "blue", "point": {"color": "blue"}}, "encoding": {"x": {"field": "Coronavirus", "title": "r/Coronavirus: blue", "type": "quantitative"}, "y": {"field": "lex", "sort": {"field": "SimDiff"}, "title": "", "type": "nominal"}}}], "data": {"name": "data-2911885988033a2c102b5d06089afd11"}, "title": "", "$schema": "https://vega.github.io/schema/vega-lite/v4.17.0.json", "datasets": {"data-2911885988033a2c102b5d06089afd11": [{"lex": "science", "Coronavirus": 0.35689404606819153, "conspiracy": 0.014754777774214745, "SimDiff": 0.3421392682939768}, {"lex": "research", "Coronavirus": 0.23729760944843292, "conspiracy": 0.07461787760257721, "SimDiff": 0.1626797318458557}, {"lex": "vaccines", "Coronavirus": 0.08601615577936172, "conspiracy": -0.046296343207359314, "SimDiff": 0.13231249898672104}, {"lex": "sars", "Coronavirus": 0.07451134920120239, "conspiracy": -0.021860774606466293, "SimDiff": 0.09637212380766869}, {"lex": "distancing", "Coronavirus": 0.032162513583898544, "conspiracy": -0.062317121773958206, "SimDiff": 0.09447963535785675}, {"lex": "corona", "Coronavirus": 0.015173436142504215, "conspiracy": -0.07627764344215393, "SimDiff": 0.09145107958465815}, {"lex": "pandemic", "Coronavirus": 0.032166216522455215, "conspiracy": -0.049691323190927505, "SimDiff": 0.08185753971338272}, {"lex": "lockdowns", "Coronavirus": 0.05210113525390625, "conspiracy": -0.016655955463647842, "SimDiff": 0.06875709071755409}, {"lex": "rona", "Coronavirus": 0.01636863872408867, "conspiracy": -0.04496964067220688, "SimDiff": 0.06133827939629555}, {"lex": "masks", "Coronavirus": 0.04734383150935173, "conspiracy": -0.0035551167093217373, "SimDiff": 0.05089894821867347}, {"lex": "spreader", "Coronavirus": -0.05658354610204697, "conspiracy": -0.08908845484256744, "SimDiff": 0.03250490874052048}, {"lex": "maskless", "Coronavirus": 0.0035706909839063883, "conspiracy": -0.017772605642676353, "SimDiff": 0.021343296626582742}, {"lex": "quarantines", "Coronavirus": 0.012220581993460655, "conspiracy": 0.01386572141200304, "SimDiff": -0.001645139418542385}, {"lex": "moderna", "Coronavirus": 0.022082751616835594, "conspiracy": 0.029249070212244987, "SimDiff": -0.007166318595409393}]}}, {"mode": "vega-lite"}); </script>

# proj_sims_chart.save(f'../out/proj-emb_{models[0]["name"]}--{models[1]["name"]}___{pole_words[0]}--{pole_words[1]}.pdf')

wuqui/neocov

NeoCov

Description

Imports

Variables

Detecting semantic change

Read comments

Preprocessing

Train models

Create corpus

Train model

Save model

Load models

Align models

Measure distances

Inspect neighbourhood

Social semantic variation

Covid-related communities

read comments

get subreddit counts

Train models

Load models

Align models

Semantic neighbourhoods

words that differ the most between both communities

nearest neighbours for target lexemes in both communities

biggest discrepancies in nearest neighbours for target lexemes

Maps of social semantic variation

common neighbours

differences in neighbours

Dimensions of social semantic variation

good vs bad

objective vs subjective