Indian journalists’ influence on Twitter

(suggested citation: Mishra, D. and Pal, J. (2020) Indian journalists’ influence on Twitter https://joyojeet.people.si.umich.edu/indian-journalists-influence-on-twitter/)

 

Overview
The goal of this work has been to map the networks of influence on Indian social media. We took the top 110 journalists based on the number of other journalists who follow them from our sample of 1899 journalists to create a seed set of key journalists in India. A list of the journalists who were key influencers in terms of “following” behaviour is available here.
This list shows us a landscape of journalists with important voices, but also highlights several key problems with the sample, a significant leaning towards English-language journalists. We did a follow-up to seek an alternate means of understanding “influence” to seek out which journalists were retweeted by key politicians. The results are available here, and this list has a far higher number of journalists whose primary work is not in English.
In line with other ways of understanding “influence” we mapped key journalists’ social media behavior and following to seek their footprint in the months of January and February 2020. By footprint, we measured their median retweet rate (as opposed to arithmetic mean which would be biased by viral tweets), as well as the total number of retweets to see how much a journalist gets mentioned.
Findings
As we see in figure 1, a small number of journalists have a hugely outsized impact in terms of the net retweets their messages get – Rana Ayyub, Arfa Khanum, Faye D’Souza and Abhisar Sharma get fairly significant engagement to their messaging. However, some less obvious accounts are also important. Vinod Jose, Somesh Jha, Nitin Sethi, Arvind Gunasekar, Supriya Sharma and Milind Khandekar all have significant footprint despite relatively much smaller following.

Indian Journalists arranged by their retweet rate, size of the bubbles is proportionate to their total online following
Figure 1: Indian Journalists arranged by their retweet rate, size of the bubbles is proportionate to their total online following
We see a dominance of television and digital journalists (which are often made up of people who were formerly in some traditional format) as compared to those who are largely print journalists. We excluded from our sample commentators who were widely retweeted. The categorization of journalists into these was somewhat arbitrary, and we classified as “mixed” journalists whose work typically appeared across formats.
The reasons for being retweeted can be varied, and it is one, albeit incomplete measure of online influence. Retweets can be as a result of mastering short-form messaging, having a niche following, or being part of a topic or position that typically trends. Thus some of the most retweeted journalists are also routinely among the most trolled and harassed. For instance, two commentators who have mastered the short form, particularly using a mix of are Akash Banerjee and Dhruv Rathee, both score higher in median retweets than almost all the journalists in this list.
The frequency of tweeting also impacts the amount of retweets one gets. For instance, accounts that tweet fairly frequently (not including retweets), such as Shivam Vij, Suhasini Haider, and Seema Chisti get relatively fewer median retweets. The most prolific tweeter was the Wire’s Rohini Singh who tweeted and engaged in conversation with people on Twitter almost twice as much as any other journalist on this list.
We also looked at two other metrics. One, whose messages were more “political” for which we used a crude metric of the use of politicians’ handles, as opposed to other methods such as a “bag of words model” or hasthtag mapping to do a more complete topical model. Ashutosh and Rajat Sharma are among journalists who mention politicians in a very high proportion of their tweets.
Finally, we see that the use of URLs is another important metric. Journalists who use a high proportion of links are typically providing a link to a story that has more information, thus acts as a plug for both the story and the organization. Shekhar Gupta, Ashutosh, Sidharth Varadarajan, Nikhil Wagle, and Paronjoy Guha Thakurta all include URLs, typically links to reports from their respective organizations, in a majority of their tweets.
Methodology
Using Twitter’s public API we sourced their tweets(~110k) for the months of January and February 2020. We removed all accounts that tweeted less than 300 times in this period, to focus on the most active social media accounts. We only count retweets of original tweets, not of retweets of other persons’ tweets. More methodological details are in previous articles in this series here.

Table 1: Key statistics for various journalists and their online influence

Rank (median RTs) ID Total Tweets

(Jan-Feb)

% Tweets that are Retweets

 

Number of politicians mentioned Followers (start of period) % Tweets with URLs Sum RTs Median RTs
1 khanumarfa 637 58% 75 355688 19% 237399 1133
2 RanaAyyub 1278 26% 30 876194 29% 756470 826
3 abhisar_sharma 2267 90% 337 713813 15% 222694 562
4 fayedsouza 481 7% 25 896593 59% 313034 391.5
5 Nidhi 1780 60% 168 609467 14% 160440 268
6 BDUTT 823 15% 148 7068088 29% 278118 242
7 ashutosh83B 544 15% 666 1997404 75% 215951 233.5
8 rahulkanwal 336 55% 33 4428992 13% 173344 230.5
9 rohini_sgh 5923 49% 771 280800 12% 364277 186.5
10 sardesairajdeep 778 13% 134 8871256 41% 387289 179
11 SreenivasanJain 573 28% 24 565280 32% 130002 129
12 free_thinker 2128 49% 66 216735 33% 218752 104
13 bhogleharsha 643 34% 2 8440876 14% 68572 102.5
13 gauravcsawant 768 21% 99 1422889 22% 59278 102.5
15 rahulpandita 686 49% 20 97629 15% 49756 98
16 mkvenu1 594 34% 68 143452 32% 46606 91.5
17 milindkhandekar 623 24% 36 100282 39% 88212 91
18 TheJaggi 1824 89% 194 160585 15% 54391 90
19 AdityaRajKaul 2191 30% 144 252494 18% 240469 77
20 sagarikaghose 601 30% 100 4117016 44% 99292 73.5
21 karunanundy 1039 34% 106 123919 30% 49407 68
22 RajatSharmaLive 369 4% 212 3849460 13% 61715 66
22 arvindgunasekar 728 32% 37 53243 31% 113224 66
24 smitaprakash 1462 30% 42 751123 17% 138850 64
25 sharmasupriya 486 42% 2 45625 51% 22350 53
26 waglenikhil 1167 31% 83 840941 63% 96982 50
27 ShivAroor 1305 20% 39 898206 23% 101971 49
28 abhijitmajumder 988 11% 41 539513 33% 135946 48
29 vinodjose 936 89% 30 21337 11% 16478 39
29 YRDeshmukh 1968 45% 125 175796 14% 62280 39
31 nit_set 1570 86% 120 28656 11% 16110 38.5
32 someshjha7 1296 60% 108 20809 17% 28369 38
33 MnshaP 1411 63% 53 40088 20% 26342 34.5
33 iamnarendranath 748 41% 64 51670 13% 33088 34.5
35 soniandtv 337 67% 10 834328 31% 10859 23
36 suhasinih 2581 66% 122 1282035 24% 60680 22
37 svaradarajan 1323 35% 98 544143 65% 59129 21
38 tavleen_singh 419 44% 18 615776 48% 27200 20
39 vijaita 1508 46% 95 83482 25% 32773 18
39 saikatd 1563 65% 75 162027 18% 26975 18
41 DilliDurAst 4806 68% 337 97314 22% 98581 17
42 nilanjanaroy 1224 34% 98 215888 33% 29130 16.5
43 paranjoygt 1415 41% 79 80931 61% 25177 14
44 PritishNandy 1294 57% 146 1953861 10% 22035 13.5
45 ShekharGupta 1669 9% 44 2215505 85% 97369 13
46 suchetadalal 928 52% 124 689273 29% 8538 12.5
47 seemay 2511 64% 119 30289 29% 41764 11
47 manupubby 793 51% 44 43399 25% 7105 11
50 vikramchandra 867 73% 73 2993692 21% 5361 10
50 mihirssharma 448 70% 15 155097 21% 2906 10
50 maya206 727 58% 63 68479 32% 12145 10
50 MasalaBai 1473 38% 71 72275 14% 16676 10

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