## 6 Applying the Transformer-Based Models to Twitter Data  
### Twitter and Abstractive Summarization  
Social media platforms today provide a high variety of platform affordances [56], which implies high variability in length and structure of user posts. For instance, Twitter limits the post/comment volume at 280 characters, which affects how users shape their speech.  
Previous studies that used Twitter data for topic modeling to solve communication science tasks, e.g., for agenda detection purposes or finding pivotal moments in networked discussions [11], have had only moderate success. Moreover, social media data, and especially the noisy, unstructured, and extremely short texts from microblogs such as Twitter, have created multiple complications for topicality, sentiment, and opinion detection [57]. This implies that other methods, such as text summarization, the success of which does not depend that much on text length, may be applied to discussion evolution studies with better results.  
Nevertheless, the studies of text summarizations beyond Reddit are scarce; of all platforms, Twitter is next to attract the researchers' attention. Neural-network abstractive summarization for Twitter has been explored in [58], and Reddit and Twitter were compared in [59]. These studies have shown the principal possibility of applying abstractive summarizations to Twitter data, but several important gaps have remained unresolved.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Post Title** & **Post Summary** & **Comment Summary** \\ \hline At these levels, it is NOT about the price of the stock. It is the number of shares in the hedge funds’ possession. & Weird price is now around where the volume dropped off. I think we’re are the bottom, as long as we hold. & This is not about the market, it’s about how much you are willing to pay attention to your own product. \\ \hline People are risking their lives to wage war against the suits and it brings tears to my eyes to watch them do it. & The basards who steal thousands of years off the regular man’s lives every single day. & I don’t know how to get a job, but I think that’s why I hate the f’ck out of it. \\ \hline I suspect the hedgies are illegally covering their short positions & There is far more nefarious shit at play:** So this morning I saw the S3 and put into the SEC has been for a long time, but if’s not worth it. & I agree with you, but I don’t believe in this. \\ \hline \hline
30 Seconds From Triggering Market Nuclear Bomb & this is a big deal. & I’m a nutjob. \\ \hline \hline  & Yes, hedge funds are pretty good for hedge fund management. But they can’t be managed with the same amount of foreclosing on the 28 th RH is not an actual Last Thursday, Why They Didn’t Cover on Friday, and Why They Didn’t do T think They Didn (GME) find out how much money they need to put in between \$350 and \$400 M positions. & This is not a great thing. EDIT: I just wrote this on my phone, but it’s all about the money you’re looking for! \\ \hline \hline \end{tabular}
\end{table}
Table 5: Examples of summaries for comment pools.  
Of them, we address two that relate to the real-world features of Twitter discussions on the global scale. First, when such discussions erupt, they quickly become multilingual [60]. However, despite Twitter being extremely multi-lingual both within and beyond such discussions, there are no studies that apply abstractive summarization to tweets on multiple languages. Second, tweets are too short to summarize, but their accumulation into author, thread, or sub-topic pools, in line with the concept of cumulative deliberation [61], is what matters within heated discussions for public opinion formation. However, it has not been tested how the neural-network summarization models would work on the pools of tweets.  
Thus, addressing these two gaps, we have conducted tests of abstractive summarization for a discussion on Twitter dedicated to the _Charlie Hebdo_ massacre in Paris.