Dear Biswajit,

I hope you are doing good.

Seems like it is the issue m_norm argument in kmeans() instead use corpus_txfidf and check.
 
Please try with the modified code below:

DB<- read.csv("NFL_SocialMedia_sample_data1.csv", head=T )
head(DB)
str(DB)
library(tm)
social_text <-data.frame(DB$content)
social_corpus <- VectorSource(social_text)
corpus <- Corpus(VectorSource(social_text))
print(corpus)
inspect(corpus)
corpus_clean1 <- tm_map(corpus,tolower)
corpus_clean2 <- tm_map(corpus_clean1,removeWords,stopwords("english"))
corpus_clean3 <- tm_map(corpus_clean2,removePunctuation)
corpus_clean4 <- tm_map(corpus_clean3,stripWhitespace)
corpus_clean5 <- tm_map(corpus_clean4,removeNumbers)
inspect(corpus_clean5)
corpus_dtm <- DocumentTermMatrix(corpus_clean5)
corpus_txfidf <- weightTfIdf(corpus_dtm)
inspect(corpus_txfidf)
m <- as.matrix(inspect(corpus_txfidf))
m
norm_eucl <- function(m) m/apply(m, MARGIN=1, FUN=function(x) sum(x^2)^.5)
m_norm <- norm_eucl(m)
cl <- kmeans(corpus_txfidf, 10)
cl

Please try it and let me know if this helps you.