Asmit Singh, Lalitha Kameswari, Jivitesh Jain, Tushar Mohan, Aravind Narayanan, Dipanwita Guhathakurta, Ponnurangam Kumaraguru, Joyojeet Pal

We examined the use of Twitter by 10426 politicians from Uttar Pradesh, across all levels of seniority, midway into the phases of polling in the Uttar Pradesh elections of 2022, starting January 1, 2022 and up until March 1, 2022. We summarize here our key findings by examining four key questions.

How does Twitter relate to Urbanization and Literacy?

To address the first question, we examined the number of urban residents by location, and levels of literacy, and find there is a statistically significant relationship between both the number (and percentage) or urban residents and the likelihood that politicians in that area use Twitter, as well as with the level of literacy and the likelihood of tweeting. We removed Lucknow district from the equations we ran, since it is an outlier being the capital where most parties have a number of listed post holders.

We see some variation — for instance, the district with the highest urban populations – Ghaziabad, adjoining Delhi, and Kanpur, both have fewer politicians online than Varanasi, the Prime Minister’s constituency, which has had a lot of mid- to lower-level politicians go online, and Allahabad / Prayagraj, which is a politically important district in the state, and home to several key politicians from across parties.

Figure 1: Number of urban residents in a district correlated with the number of politicians from that district active on Twitter

Taking the number of politicians on Twitter as the dependent variable and the urban population of the district as the independent variable,and found a strong correlation between the 2 with an R value of 0.61. The coefficient 𝛽₁ was found to be significantly different from 0 (p<0.05). (See Appendix Table 1)

Figure 2: Percentage of urban residents in a district correlated with the number of politicians from that district active on Twitter

When we ran the same regression to correlate the percentage of urban residents to the number of politicians active on Twitter, again we found a strong statistically significant (p<0.05) relationship between the two variables with an R value of 0.37. There are outliers. On the lower end, we see that Gautam Buddha Nagar (Noida) has a smaller share of politicians active online (partly because of the cross-listing with Delhi) whereas Gorakhpur, an overwhelming majority rural district has a high number of politicians active on Twitter. This again can be attributed to CM Yogi Adityanath, who contests from the district. Please see Appendix 2 for details.

We see the same relationship between the number of literate persons in a district and the number of politicians. The relationship here is the strongest, statistically significant (p<0.05) with an R value of 0.78. Figure 3 below shows us a visualization of the districts. Again, we see that Varanasi is an outlier, for the reasons outlined above. Please see Appendix 3 for details.

Figure 3: Number of literate residents in a district correlated with the number of politicians from that district active on Twitter

As a sanity check we ran the same regression on the share of literate persons in a district, we again find a statistically significant relationship (p<0.05) with a strong relationship of rate of literacy with the n of politicians on Twitter at an R value of 0.78. Please see Appendix 4 for details.

Figure 4: Percentage of literate residents in a district correlated with the number of politicians from that district active on Twitter

Where’s the Action?

To detail these findings, we detail the data by district. Using a crude method of looking at the number of active politicians and the rate at which they get retweeted, we examined the districts in which one or another party had a clear Twitter dominance. We restricted our analysis to those districts that had 50 or more active politicians online. The districts which had a clear BJP Twitter advantage (where the local networks of politicians on Twitter were larger, at least two of the top three accounts belonged to that party, and the party was more active on social media) were Ghaziabad, Amethi, Mathura, Barabanki, Kaushambi, Gorakhpur. The districts where the Samajwadi Party had a clear advantage were Banda, Fatehpur, Etawah, Prayagraj (Allahabad), and Azamgarh. The districts where the Twitter battles were evenly matched across multiple parties were Kannauj, Ballia, Lucknow, Sultanpur.

District(min. 40 active politicians)	Code	SPPoliticians	BJP Politicians	Other Party Politicians	SP per- politician median Tweet count	BJP per- politician median Tweet count	Other party per- politician median Tweet count
Agra	AGR	49	125	52	49	111	179.5
Aligarh	ALG	14	51	26	150	82	111
Prayagraj	ALH	123	84	59	100	53.5	142
Amethi	AMT	15	91	53	58	78	85
Ayodhya	AYD	28	74	14	28.5	165	162
Azamgarh	AZA	53	15	25	68	141	329
Bahraich	BAH	40	23	7	59	102	3
Ballia	BAL	26	33	25	165.5	115	131
Balrampur	BRP	12	14	24	36	108	212
Banda	BAN	25	27	16	105	65	84
Barabanki	BAR	26	61	19	40	78	63
Bareilly	BRL	33	32	19	34	68	157
Basti	BAS	21	76	28	17	120	241.5
Bhadohi	BDH	12	32	10	91	119.5	235
Chandauli	CHD	14	29	9	21	117	26
Deoria	DEO	33	63	17	53	97	69
Etawah	ETW	37	15	6	80	94	159
Fatehpur	FTH	34	19	7	77.5	130	128
Ghaziabad	GZB	23	121	47	118	211	122
Ghazipur	GZP	25	40	15	77	83.5	68
Gonda	GON	20	47	17	52	120	109
Gorakhpur	GOR	34	124	27	88.5	130.5	159
Hapur	HAP	7	31	27	44	205	199
Hardoi	HAR	21	53	12	59	58	98.5
Hathras	HAT	4	17	10	320.5	145	86
Jaunpur	JNP	66	100	43	90	84.5	145
Jhansi	JHA	26	28	26	48.5	177.5	93.5
Kannauj	KAN	26	26	8	109	102	97.5
Kanpur Dehat	KND	15	16	6	29	288.5	283
Kanpur Nagar	KNN	58	81	24	63	136	74.5
Kaushambi	KAU	7	30	7	50	138	150
Lucknow	LCK	442	406	226	113.5	158	147.5
Mathura	MAT	12	53	38	19.5	89	102.5
Meerut	MRT	21	77	38	96	137	197
Mirzapur	MIR	17	39	20	56	41	109.5
Moradabad	MOR	27	48	14	32	80.5	92
Muzaffarnagar	MUZ	13	26	30	56	213	144.5
Pratapgarh	PGH	22	44	14	96.5	75	19
Rampur	RAM	11	29	14	28	103	80
Saharanpur	SAH	13	42	20	60	137.5	185
Shahjahanpur	SHJ	15	23	12	26	85	127.5
Sitapur	SIT	24	28	14	28	123	144.5
Sonbhadra	SON	10	37	12	34.5	230	500.5
Sultanpur	SUL	24	30	16	30	81	101.5
Unnao	UNN	16	32	8	59.5	34.5	148.5
Varanasi	VAR	87	137	74	69	111	162.5

These data points may be skewed by the effectiveness of IT cells or small cores of supporters. It is also true that the INC’s IT cell is fairly well organized and networked, but most on-the-ground analyses seem to discount its likelihood of winning seats proportionate to its social media footprint. However, these data underline the massive dominance that the BJP has over other parties on Twitter.

District Code	Top most followed active state politician handle from the district	Party	2nd most followed active state politician handle from the district	Party	3rd most followed active state politician handle from the district	Party
JAL	Rashmipal_86	SP	bpsvermaoffice	BJP	mohammad_ubaid_	INC
LAL	ArchanaPatelAD	APNA	inckuldeepnsui	INC	nitinpanthltp	BJP
HAM	DipakSurjeet	SP	Aryan_Vaishnav9	INC	arunodayINC	INC
KSH	RadheshyaSingh	SP	Sunny10113594	BJP	Bjp4shubhamsahi	BJP
ETA	ashishyadavmla	SP	visheshYSP	SP	AsRathoreBJP	BJP
CHK	BhaironMishra	BJP	KishanGupta_INC	INC	AtuleshanandM	BJP
FRZ	DrChandrasenBJP	BJP	Prashan25958727	SP	RohitYadav5500	SP
GBN	satender_awana	BJP	Sandeepsharmak7	BJP	AshuChhawri1717	BJP
BAH	NitishRastogi01	BJP	Shivafauji	BJP	nishanktripath1	BJP
MAH	yadavyogi1	SP	ManojA2Y	SP	Neerajbuddh_ASP	BSP
KAS	inderveshverma	BJP	ABVPVikas89	BJP	TejendraLodhi	BJP
AUR	AAPlogical	AAP	manju27bjp	BJP	Narendralodh73	BJP
BRL	sachink71656735	BJP	JabirAkhtarKhan	AIMIM	DrPramendr	BJP
AMR	SatyadeoPawar	BJP	VishalVermaJi00	BJP	kstanwarmp	BJP
HAP	anandktrbjp	BJP	ramesh4Churu	BJP	SarswatPrafful	BJP
KAN	dineshsngyadav	SP	poonamy49617468	SP	socialistarchna	SP
BIJ	Ashokkatariya9	BJP	Digvija27100456	BJP	BRBJPBIJNOR	BJP
KND	Up32Wale	BJP	iABHIKSHA	BJP	AnkitSinghZ	BJP
HAT	SharadBJPoffice	BJP	islamkhan919	SP	RamAvtarValmiki	INC
BAS	pal_jagdambika	BJP	Shweta4Nation	BJP	BajrangbaliMa	BJP
BAN	Shubham41489417	BJP	HariNMishra	BJP	AAPkaRamGupta	AAP
BRP	Dr_Mannan_	AIMIM	HKhan_293	INC	Vibhuonline	INC
AZA	amitydvsp	SP	IamShaikhAdil	AIMIM	NishantRaii	SP
BDH	ashutoshgyanpur	BJP	AkashDubeyBJP	BJP	ratnakarbjp	BJP
HAR	jeetusp	SP	bjpsaurabh12	BJP	PrachiPretesh	BJP
FTH	MahipalMahla	BJP	anjuydv	SP	YunusSamajwadi	SP
GON	gopalkagarwal	BJP	KVSinghMPGonda	BJP	BNSinghbobby	BJP
ALG	SatishGautamBJP	BJP	kuldeepgaurBjp	BJP	LakhendraMeena	INC
AMT	DeepakSinghINC	INC	BJP4Amethi	BJP	dtripathi264	BJP
MAI	spsunilyadav	SP	LalitMYOfficial	SP	Samajwadi_mpi	SP
CHD	DrMNPandeyMP	BJP	mlasadhanasingh	BJP	IshanMilki	SP
JHA	Socialist_arun	SP	AnuraagJhansi	BJP	pradeepjain52	INC
GZP	Virendra_mla	SP	manvendrabjp	BJP	rajeshysp	SP
GOR	myogiadityanath	BJP	AgrawalRMD	BJP	kamleshpassi67	BJP
KAU	BJPVinodSonkar	BJP	BabluGargBjp	BJP	prashant3k1	BJP
BAR	priyankaMP_BBK	BJP	upendrasinghMP	BJP	PushpendraBjpKm	BJP
DEO	manishagautamak	BSP	msirsiwal	INC	BirendraYdvSP	SP
AMB	mpriteshpandey	BSP	ankitchandelbjp	BJP	12tiwari25	AAP
AGR	BjpRajora	BJP	AbhilashaBJP	BJP	Rajkumarchahar9	BJP
MAI	yadavakhilesh	SP	SubratPathak12	BJP	RanjeetYadavSP1	SP
BAL	sparvindgiri	SP	upendratiwari_	BJP	virendramastmp	BJP
AYD	pawanpandeysp	SP	LalluSinghBJP	BJP	PanditSamarjee2	SP
KNN	sdPachauri1	BJP	ptopmishra	SP	manojku72692521	SP
BAG	Phulesavitribai	KBMP	AnilTomarBjp	BJP	RLD_Baghpat	RLD
ALH	Vndnason	AAP	yogita_singh13	SP	RakshaMantri	SP
ETW	socialistaditya	SP	kuwarprashant	SP	Samajwadi_AY	SP
JNP	YRBSamajwadi	SP	OsamaShaikhIND	INC	Hb_o12	BJP
GZB	Gen_VKSingh	BJP	druditatyagi	BJP	preeti_chobey	SP
LCK	CMOfficeUP	BJP	BJP4UP	BJP	samajwadiparty	SP

Who does the party think gets the votes, Modi or Yogi?

We examined the number of tweets that referred to Narendra Modi and Yogi Adityanath to get a sense of who the party expects will win the elections. The results show that while Yogi Adityanath had a higher footprint early in the campaign in January, that got replaced by Modi by February, to the point where Modi is now consistently the more featured of the two leaders in terms of both the engagement by party politicians and workers, and equally importantly, by the party’s main social media handle.

Figure 5: Line graph of the number of times Narendra Modi and Yogi Adityanath are mentioned in tweets by BJP politicians through all UP districts during the study time period. We find six events corresponding to the peaks (calculated over two day interval):

1.  Jan 6, Modi peaks for 150 crore milestone for covid vaccinations

2.  Jan 9 Yogi peaks for communal 80 vs 20 tweet about BJP’s win in UP

3.  Jan 14 Yogi peaks for Magh mela, announcing contest from Gorakhpur

4.  Jan 26 Modi peaks for Republic day and UP state formation day address

5.  Feb 1 Modi peaks for virtual rallies

6.  Feb 9 Modi peaks for televised ANI interview

7.  Feb 23 Modi peaks for public gatherings in Prayagraj, Amethi, Hardoi etc

We also checked for this pattern in the main handle for the BJP in the state – @bjp4UP. Here, we see that Modi was virtually missing from the discourse in much of January, and re-emerged only in February, and since mid-February has in fact been more important in the party’s outreach than the Chief Minister.

Figure 6: Visualization of the number of mentions weekly of @narendramodi and @myogiadityanath by @BJP4UP handle in between Dec 31 and March 3.

We visualized the words that appeared most commonly in the tweets of that included @narendramodi and @myogiadityanath during the study period, excluding common tweets and we found that the co-occurring tweets tell a very interesting story about the way BJP leaders and the party establishment sees the two figures in its outreach. To arrive at these, we merged synonyms (thus सपा is merged with समाजवादी), we remove common words articles, common verbs (thus में, श्री, और, है etc) as well as words that are purely informational (चुनाव, जनसभा, मतदान, सत्ता etc) and words that have no political consequence in the context of the tweets (प्रदेश, लोग etc), and are left with terms that are either explicitly part of the political discourse, or are about locations of relevance.

Figure 7: Wordcloud visualization of co-occurring words in tweets including @myogiadityanath – size of the words proportionate to the number of mentions

We can see that the differences between the two leaders and the tweets mentioning them are significant. While there are common themes (Kisan, Garib, Vikas) we can see that there are two striking differences. First there are differences in the locations that the two sets of tweets mention Saharanpur, Kashi (his constituency), the Yogi-centric tweets mention Gorakhpur (his constituency), Ayodhya, Muzaffarnagar.

Second, we see that the Modi-centric tweets take aim at the familial continuity of power (through the use of “parivarvaad”) – aimed at both Akhilesh and Priyanka, but primarily at the former. While both leaders refer to farmers, they are a lot more central to Modi-centric messaging. Likewise while “Corona” and “Vaccine” also on tweets about both leaders, the centrality of vaccine success, as well as the touting of marquee initiatives like Atmanirbhar are more seen in Modi-centric messaging.

Finally, the Yogi-centric messaging has one overarching theme — that of sectarianism. We see a number of terms either referring to Samajwadi party in veiled references to rioters, terrorists and criminals through the use of terms like अपराधियों, आतंकियों, माफिया, दंगाइयों, सुरक्षा but in addition, the Yogi-centric tweets are also more likely to use words such as भगवान,  रामभक्तों, शिलान्यास, हिंदू, मंदिर etc.

Figure 8: Wordcloud visualization of co-occurring words in tweets including @narendramodi – size of the words proportionate to the number of mentions

Of all the major contenders, probably the least active on social media is the Bahujan Samaj Party, and a number of their candidates are not active on Twitter. In general, Akhilesh Yadav is far more engaged than the two key other opposition leaders – Priyanka Gandhi and Mayawati, however, Priyanka Gandhi has gradually closed the gap with Akhilesh.

Appendix / Statistical Details

Slope	6.20e-05
Intercept	46.16
R value (Pearson’s correlation)	0.611
R Squared	0.374
P Value	2.089e-07
Standard Error	1.054e-05

Table 1. Regression table for  Politicians on Twitter = 𝛽0 + 𝛽1 * Urban Population + 𝜀𝑖

Slope	1.67
Intercept	50.72
R value (Pearson’s correlation)	0.37
R Squared	0.14
P Value	0.003
Standard Error	0.54

Table 2. Regression table for  Politicians on Twitter = 𝛽0 + 𝛽1 * Percentage Urban Population + 𝜀𝑖

Slope	7.24e-05
Intercept	-32.78
R value (Pearson’s correlation)	0.78
R Squared	0.61
P Value	1.19e-13
Standard Error	7.52e-06

Table 3. Regression table for  Politicians on Twitter = 𝛽0 + 𝛽1 * Literate Population + 𝜀𝑖

Slope	2.51
Intercept	-84.66
R value (Pearson’s correlation)	0.07
R Squared	0.61
P Value	0.03
Standard Error	1.17

Table 4. Regression table for Politicians on Twitter = 𝛽0 + 𝛽1 * Percentage Literate Population + 𝜀𝑖

Methodology for comparing the mentions of Narendra Modi and Yogi Adityanath by the BJP politicians

We collected all the tweets of BJP politicians over a period of around two months (from 31st Jan 2022 to 3rd Mar 2022) and counted the number of mentions which Narendra Modi and Yogi Adityanath got. We plotted this data keeping a rolling 2-day average to capture the trends in increase and decrease of mentions and correlated the peaks with relevant events.