UP Elections 2022

Midpoint Melodrama: UP Political Twitter Halfway through Polling Phases

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 𝛽was found to be significantly different from 0 with 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)CodeSPPoliticiansBJP PoliticiansOther Party PoliticiansSP per- politician median Tweet countBJP per- politician median Tweet countOther party per- politician median Tweet count
AgraAGR491255249111179.5
AligarhALG14512615082111
PrayagrajALH123845910053.5142
AmethiAMT159153587885
AyodhyaAYD28741428.5165162
AzamgarhAZA53152568141329
BahraichBAH40237591023
BalliaBAL263325165.5115131
BalrampurBRP12142436108212
BandaBAN2527161056584
BarabankiBAR266119407863
BareillyBRL3332193468157
BastiBAS21762817120241.5
BhadohiBDH12321091119.5235
ChandauliCHD142992111726
DeoriaDEO336317539769
EtawahETW371568094159
FatehpurFTH3419777.5130128
GhaziabadGZB2312147118211122
GhazipurGZP2540157783.568
GondaGON20471752120109
GorakhpurGOR341242788.5130.5159
HapurHAP7312744205199
HardoiHAR215312595898.5
HathrasHAT41710320.514586
JaunpurJNP66100439084.5145
JhansiJHA26282648.5177.593.5
KannaujKAN2626810910297.5
Kanpur DehatKND1516629288.5283
Kanpur NagarKNN5881246313674.5
KaushambiKAU730750138150
LucknowLCK442406226113.5158147.5
MathuraMAT12533819.589102.5
MeerutMRT21773896137197
MirzapurMIR1739205641109.5
MoradabadMOR2748143280.592
MuzaffarnagarMUZ13263056213144.5
PratapgarhPGH22441496.57519
RampurRAM1129142810380
SaharanpurSAH13422060137.5185
ShahjahanpurSHJ1523122685127.5
SitapurSIT24281428123144.5
SonbhadraSON10371234.5230500.5
SultanpurSUL2430163081101.5
UnnaoUNN1632859.534.5148.5
VaranasiVAR871377469111162.5
Table 1: Key Districts (minimum 40 active politicians on Twitter online) with the number of politicians or political workers identified, and the median rates of retweets (of original tweets) for the period 2021-01-01 to 2022-03-01.

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 CodeTop most followed active state politician handle from the districtParty2nd most followed active state politician handle from the districtParty3rd most followed active state politician handle from the districtParty
JALRashmipal_86SPbpsvermaofficeBJPmohammad_ubaid_INC
LALArchanaPatelADAPNAinckuldeepnsuiINCnitinpanthltpBJP
HAMDipakSurjeetSPAryan_Vaishnav9INCarunodayINCINC
KSHRadheshyaSinghSPSunny10113594BJPBjp4shubhamsahiBJP
ETAashishyadavmlaSPvisheshYSPSPAsRathoreBJPBJP
CHKBhaironMishraBJPKishanGupta_INCINCAtuleshanandMBJP
FRZDrChandrasenBJPBJPPrashan25958727SPRohitYadav5500SP
GBNsatender_awanaBJPSandeepsharmak7BJPAshuChhawri1717BJP
BAHNitishRastogi01BJPShivafaujiBJPnishanktripath1BJP
MAHyadavyogi1SPManojA2YSPNeerajbuddh_ASPBSP
KASinderveshvermaBJPABVPVikas89BJPTejendraLodhiBJP
AURAAPlogicalAAPmanju27bjpBJPNarendralodh73BJP
BRLsachink71656735BJPJabirAkhtarKhanAIMIMDrPramendrBJP
AMRSatyadeoPawarBJPVishalVermaJi00BJPkstanwarmpBJP
HAPanandktrbjpBJPramesh4ChuruBJPSarswatPraffulBJP
KANdineshsngyadavSPpoonamy49617468SPsocialistarchnaSP
BIJAshokkatariya9BJPDigvija27100456BJPBRBJPBIJNORBJP
KNDUp32WaleBJPiABHIKSHABJPAnkitSinghZBJP
HATSharadBJPofficeBJPislamkhan919SPRamAvtarValmikiINC
BASpal_jagdambikaBJPShweta4NationBJPBajrangbaliMaBJP
BANShubham41489417BJPHariNMishraBJPAAPkaRamGuptaAAP
BRPDr_Mannan_AIMIMHKhan_293INCVibhuonlineINC
AZAamitydvspSPIamShaikhAdilAIMIMNishantRaiiSP
BDHashutoshgyanpurBJPAkashDubeyBJPBJPratnakarbjpBJP
HARjeetuspSPbjpsaurabh12BJPPrachiPreteshBJP
FTHMahipalMahlaBJPanjuydvSPYunusSamajwadiSP
GONgopalkagarwalBJPKVSinghMPGondaBJPBNSinghbobbyBJP
ALGSatishGautamBJPBJPkuldeepgaurBjpBJPLakhendraMeenaINC
AMTDeepakSinghINCINCBJP4AmethiBJPdtripathi264BJP
MAIspsunilyadavSPLalitMYOfficialSPSamajwadi_mpiSP
CHDDrMNPandeyMPBJPmlasadhanasinghBJPIshanMilkiSP
JHASocialist_arunSPAnuraagJhansiBJPpradeepjain52INC
GZPVirendra_mlaSPmanvendrabjpBJPrajeshyspSP
GORmyogiadityanathBJPAgrawalRMDBJPkamleshpassi67BJP
KAUBJPVinodSonkarBJPBabluGargBjpBJPprashant3k1BJP
BARpriyankaMP_BBKBJPupendrasinghMPBJPPushpendraBjpKmBJP
DEOmanishagautamakBSPmsirsiwalINCBirendraYdvSPSP
AMBmpriteshpandeyBSPankitchandelbjpBJP12tiwari25AAP
AGRBjpRajoraBJPAbhilashaBJPBJPRajkumarchahar9BJP
MAIyadavakhileshSPSubratPathak12BJPRanjeetYadavSP1SP
BALsparvindgiriSPupendratiwari_BJPvirendramastmpBJP
AYDpawanpandeyspSPLalluSinghBJPBJPPanditSamarjee2SP
KNNsdPachauri1BJPptopmishraSPmanojku72692521SP
BAGPhulesavitribaiKBMPAnilTomarBjpBJPRLD_BaghpatRLD
ALHVndnasonAAPyogita_singh13SPRakshaMantriSP
ETWsocialistadityaSPkuwarprashantSPSamajwadi_AYSP
JNPYRBSamajwadiSPOsamaShaikhINDINCHb_o12BJP
GZBGen_VKSinghBJPdruditatyagiBJPpreeti_chobeySP
LCKCMOfficeUPBJPBJP4UPBJPsamajwadipartySP
Table 2: Top three most followed active Twitter accounts by district (minimum 40 active politicians on Twitter online) for the period 2021-01-01 to 2022-03-01.

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.

Figure 9: Mention counts of Akhilesh Yadav, Priyanka Gandhi, and Mayawati over time.

Appendix / Statistical Details

Slope6.20e-05
Intercept46.16
R value (Pearson’s correlation)0.611
R Squared0.374
P Value2.089e-07
Standard Error1.054e-05
Table 1. Regression table for  Politicians on Twitter = 𝛽0 + 𝛽1 * Urban Population + 𝜀𝑖
Slope1.67
Intercept50.72
R value (Pearson’s correlation)0.37
R Squared0.14
P Value0.003
Standard Error0.54
Table 2. Regression table for  Politicians on Twitter = 𝛽0 + 𝛽1 * Percentage Urban Population + 𝜀𝑖
Slope7.24e-05
Intercept-32.78
R value (Pearson’s correlation)0.78
R Squared0.61
P Value1.19e-13
Standard Error7.52e-06
Table 3. Regression table for  Politicians on Twitter = 𝛽0 + 𝛽1 * Literate Population + 𝜀𝑖
Slope2.51
Intercept-84.66
R value (Pearson’s correlation)0.07
R Squared0.61
P Value0.03
Standard Error1.17
Table 4. Regression table for Politicians on Twitter = 𝛽0 + 𝛽1 * Percentage Literate Population + 𝜀𝑖

Methodology for comparing mentions of Narendra Modi and Yogi Adityanath by 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.


This research was conducted by Lalitha Kameswari, Jivitesh Jain, Shaurya Dewan, Aravind Narayanan, Dipanwita Guhathakurta, and Prof. Ponnurangam Kumaraguru from IIIT Hyderabad, Asmit Kumar Singh and Tushar Mohan from IIIT Delhi, and Prof. Joyojeet Pal from the University of Michigan. This report can also be accessed on Prof. Joyojeet Pal’s blog. This is an ongoing research thread, please keep an eye out for updates.