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• Social media is becoming a key medium through which we communicate with each other: it is at the center of the very structures of our daily interactions. Yet this infiltration is not unique to interpersonal relations. Political leaders, governments, and states operate within this social media environment, wherein they continually address crises and institute damage control through platforms such as Twitter.

• With the proliferation of the internet into mass masses, social media is emerging as a potential way of communication. It provides a direct channel to politicians for communicating, connecting, and engaging with the public. The power of social media, especially Twitter and Facebook has been proved by its successful application during recent US presidential elections and Arabian countries' revolts. In India too, as the general election is about to knock at the door during early 2014, political parties and leaders are trying to harness the power of social media.

Content

The tweets have the #Politics hashtag. The collection started on 24/7/2021, and will be updated on a daily basis.

Information regarding the data

The data totally consists of 1 lakh+ records with 13 columns. The description of the features is given below | No |Columns | Descriptions | | -- | -- | -- | | 1 | user_name | The name of the user, as they’ve defined it. | | 2 | user_location | The user-defined location for this account’s profile. | | 3 | user_description | The user-defined UTF-8 string describing their account. | | 4 | user_created | Time and date, when the account was created. | | 5 | user_followers | The number of followers an account currently has. | | 6 | user_friends | The number of friends an account currently has. | | 7 | user_favourites | The number of favorites an account currently has | | 8 | user_verified | When true, indicates that the user has a verified account | | 9 | date | UTC time and date when the Tweet was created | | 10 | text | The actual UTF-8 text of the Tweet | | 11 | hashtags | All the other hashtags posted in the tweet along with #Politics | | 12 | source | Utility used to post the Tweet, Tweets from the Twitter website have a source value - web | | 13 | is_retweet | Indicates whether this Tweet has been Retweeted by the authenticating user. |

Inspiration

You can use this data to dive into the subjects that use this hashtag, look to the geographical distribution, evaluate sentiments, and look at trends.

Tweetplomacy 23 is a semantically annotated corpus of tweets capturing digital communicative interaction between international political leaders, peer groups and citizens in the wake of three major global crises: (1) the increasing emphasis on the security of energy supplies following Russia’s invasion of Ukraine; (2) the political and geo-economic consequences of the COVID-19 pandemic; (3) the intensified debate on the progression of climate change. These events occurred between 2018 and 2023, each of them marking a significant shake-up of the international system.

The dataset focuses on the strategic use of networked information on X (formerly Twitter) by executive political actors facing exogenous shocks in the context of a global crisis situation. It is extracted from an X archive covering more than 14 billion tweets collected from the 1% random sample API. To extract the dataset, we resort to a list of top executives of the political administration – heads of state, heads of government, ministers of foreign affairs – or their respective public-relations offices. Their tweets are filtered using a list of thematically relevant keywords in four languages (English, German, French, Spanish), reflecting the discourse with respect to the three crises mentioned above.

Our sample covers instances from the beginning of 2018 up to May 2023, representing statements made by leading politicians from 83 countries on all continents. As a subset, tweets published by the political leaders of the 38 member states of the OECD and the five BRICS countries (Brazil, Russia, India, China, South Africa) have been extracted. Additionally, the sample comprises a selection of 10 international organizations.

The entire data collection consists of the following files: (1) users: excel file with a list of 654 Twitter user handles(usernames) of top executives of the political administration (and/or their institutional accounts), their nationalities, functions/roles and tenure; (2) keywords: excel file with a list of 60 crisis-related keywords (five keywords for each of the three individual crises in four languages); (3) a gzipped JSONL file per language: each line in the JSONL files represents a JSON object containing metadata about a tweet matching either one or more of the user handles and one or more of the keywords in the respective language. Additionally, semantic enrichments (i.e., entities and sentiments) calculated on the basis of the tweet text are provided. The JSON object includes the following fields:

tweetId: integer
timeStamp: format ("EEE MMM dd HH:mm:ss Z yyyy")
userName: JSON object, for private persons containing the MD5 hashed of the username; for the public persons in the user list containing the username and the MD5 hashed of the username userBio: string (available only for public users in the user list)
followers: integer
friends: integer
retweets: integer
favorites: integer
replies: integer
matchingKeywords: list of strings representing the matching keywords
matchingUserMentions: list of strings representing the matching user mentions
matchingUserName: string representing the matching user names
sentiments: JSON object containing the output of the VADER sentiment analysis tool (available only for German, English and French).
entities: JSON object containing the output of Entity Fishing named entity linking tool
hashtags: list of strings containing the hashtags extracted from the tweet text
mentions: list of strings containing the mentions extracted from the tweet text
urls: JSON object containing short URLs extracted from the tweet text and their resolved URLs

The dataset may serve to track and examine the repercussions/resonance produced by the ‘digital audience’ of the most influential political leaders in the course of the three crises, thus hinting at the political and societal impact their communicative actions had in the digital realm. Additionally, changes in sentiments, argumentation and/or tonality as well as more general breakpoints of discussion might be identified by conducting in-depth analyses of the online discourse relating to each of the three debates.

Ultimately, the data may yield new insights into networks of communication among ‘online champions’ in the diplomatic community with regard to...

Imran Ahmed Khan Niazi is a Pakistani politician and former cricketer who served as the 22nd Prime Minister of Pakistan from August 2018 until April 2022. He is the founder and leader of Pakistan Tehreek-e-Insaf. I make this dataset because Imran Khan is now among Top Politicians around the world and every statement has a fruitful meaning in Pakistan many politicians use Twitter to deliver authentic statements.

It is a mixed dataset of Ex-PM of Pakistan Imran Khan tweets. it's best for practicing cleaning and for Sentiment Analysis.

Features of Dataset 1) Date 2) User 3) Tweets

The file includes data and code to replicate figures in Genovese (2019) "Politics @Pontifex: International Crises and Political Patterns of Papal Tweets", PS: Political Science & Politics, 52(1), 7-13