The title of this research is "Lexical Cohesion Used in Donald Trump's Campaign Speeches". Lexical cohesion is one of the most important tools for bringing text together. Lexical cohesion is grouped into five types. Due to the large number of types, research on lexical cohesion needs to be carried out and the problems to be studied are: the types and uses of the most common types found in Donald Trump's campaign speeches. The theory used is the theory of lexical cohesion types taken from Cohesion in English by Halliday and Hassan (1976). This study uses four of Donald Trump's speeches as data sources. Data collection is carried out in the form of library research, which searches for and downloads data sources and then reads the relevant data included in it. All data is grouped into the appropriate type group. The data that has been collected is analyzed descriptively and frequency. The results of the study show that five types of lexical cohesion are found in Donald Trump's campaign speeches. The five types of lexical cohesion found are repetition, synonym, superordinate, general words, and collocation. The mostly type of lexical cohesion found is repetition.

Automated social media accounts, known as bots, have been shown to spread disinformation and manipulate online discussions. We study the behavior of retweet bots on Twitter during the first impeachment of U.S. President Donald Trump. We collect over 67.7 million impeachment related tweets from 3.6 million users, along with their 53.6 million edge follower network. We find although bots represent 1% of all users, they generate over 31% of all impeachment related tweets. We also find bots share more disinformation, but use less toxic language than other users. Among supporters of the Qanon conspiracy theory, a popular disinformation campaign, bots have a prevalence near 10%. The follower network of Qanon supporters exhibits a hierarchical structure, with bots acting as central hubs surrounded by isolated humans. We quantify bot impact using the generalized harmonic influence centrality measure. We find there are a greater number of pro-Trump bots, but on a per bot basis, anti-Trump and pro-Trump bots have similar impact, while Qanon bots have less impact. This lower impact is due to the homophily of the Qanon follower network, suggesting this disinformation is spread mostly within online echo-chambers.

In this study, we analyze both mainstream and social media coverage of the 2016 United States presidential election. We document that the majority of mainstream media coverage was negative for both candidates, but largely followed Donald Trump’s agenda: when reporting on Hillary Clinton, coverage primarily focused on the various scandals related to the Clinton Foundation and emails. When focused on Trump, major substantive issues, primarily immigration, were prominent. Indeed, immigration emerged as a central issue in the campaign and served as a defining issue for the Trump campaign. We find that the structure and composition of media on the right and left are quite different. The leading media on the right and left are rooted in different traditions and journalistic practices. On the conservative side, more attention was paid to pro-Trump, highly partisan media outlets. On the liberal side, by contrast, the center of gravity was made up largely of long-standing media organizations steeped in the traditions and practices of objective journalism. Our data supports lines of research on polarization in American politics that focus on the asymmetric patterns between the left and the right, rather than studies that see polarization as a general historical phenomenon, driven by technology or other mechanisms that apply across the partisan divide. The analysis includes the evaluation and mapping of the media landscape from several perspectives and is based on large-scale data collection of media stories published on the web and shared on Twitter. Cross-linking patterns between media sources offer a view of authority and prominence within the media world. The sharing of media sources by users on Twitter and Facebook provides a broader perspective on the role and influence of media sources among people engaged in politics through Twitter and Facebook. The differential media sharing patterns of Trump and Clinton supporters on Twitter enable a detailed analysis of the role of partisanship in the formation and function of media structures. Content analysis using automated tools supports the tracking of topics over time among media sources. Qualitative media analysis of individual case studies enhances our understanding of media function and structure.

Collection of tweets related to the 2016 US election that went viral during the election day and until March 2017. Viral tweets are those that achieved the 1000-retweet threshold during the collection period. We queried Twitter's streaming API using the hashtags #MyVote2016, #ElectionDay, #electionnight, and the user handles @realDonaldTrump and @HillaryClinton.

Tweets have been labelled as containing fake news or not by two sets of people. A fake news is one the following:

• Serious fabrication
• Large-scale hoaxes
• Jokes taken at face value
• Slanted reporting of real facts
• Stories where the 'truth' is contentious

The research problem that the collection of this data seeks to solve includes if celebrities make good mayors. Given the rise in celebrities entering politics, this research will be beneficial to scholars who may wish to develop a new global typology for celebrity directly elected mayors and expand the discussion on directly elected mayors by further reviewing the effects of celebrity politics and legitimacy. Qualitative methods are used for this project and these involve reflection on the effects of words, implicit assumptions, and problematizations. With the exception of one CfG report, the data collected derives from publicly available policy documents, media releases, journalism, tweets to public threads, and posts to public forums. This data set includes policy documents, media releases, media articles, tweets to public threads, posts to public forums. published thesis and journal articles, public consultation conducted on Geelong's directly elected mayoral system, candidate information for the first and second mayoral elections, published biography “Mr Paparazzi and legislative documents relating to directly elected mayors. The majority of files are saved in PDF, DOC, CSV and JPEG formats.

CodeThis figshare repository hosts a collection of tools and scripts for Twitter data analysis, focusing on Election Prediction using sentiment analysis and tweet processing. The repository includes four key files:twitter_data_collection.py: This Python script is designed for collecting tweets from Twitter in JSON format. It provides a robust method for gathering data from the Twitter platform.EP.ipynb: EP.ipynb" is designed for sentiment analysis and tweet processing. It features three sentiment analysis methods: VADER, BERT, and BERTweet. It includes a US states dictionary for geolocating and categorizing tweets by state, providing sentiment analysis results in both volumetric and percentage formats. Furthermore, it offers time-series analysis options, particularly on a monthly basis. It also includes a feature for filtering COVID-19-related tweets. Additionally, it conducts election analysis at both state and country levels, giving insights into public sentiment and engagement regarding political elections.Datasetbiden and trump.csv Files:The "biden.csv" and "trump.csv" files together constitute an extensive dataset of tweets related to two prominent U.S. political figures, Joe Biden and Donald Trump. These files contain detailed information about each tweet, including the following key attributes:create_date: The date the tweet was created.id: A unique identifier for each tweet.tweet_text: The actual text content of the tweet.user_id: The unique identifier for the Twitter user who posted the tweet.user_name: The name of the Twitter user.user_screen_name: The Twitter handle of the user.user_location: The location provided by the user in their Twitter profile.state (location): The U.S. state associated with the user's provided location.text_clean: The tweet text after preprocessing, making it suitable for analysis.Additionally, sentiment analysis has been applied to these tweets using two different methods:VADER Sentiment Analysis: Each tweet has been assigned a sentiment score and a sentiment category (positive, negative, or neutral) using VADER sentiment analysis. The sentiment scores are provided in the "Vader_score" column, and the sentiment categories are in the "Vader_sentiment" column.BERTweet Sentiment Analysis: The files also feature sentiment labels assigned using the BERTweet sentiment analysis method, along with associated sentiment scores. The sentiment labels can be found in the "Sentiment" column, and the cleaned sentiment labels are available in the "Sentiment_clean" column.This combined dataset offers a valuable resource for exploring sentiment trends, conducting research on public sentiment, and analyzing Twitter users' opinions related to Joe Biden and Donald Trump. Researchers, data analysts, and sentiment analysis practitioners can utilize this data for a wide range of studies and projects.This repository serves as a resource for collecting, processing, and analyzing Twitter data with a focus on sentiment analysis. It offers a range of tools and datasets to support research and experimentation in this area.

The following dataset is prepared for the paper titled 'Fear-Anger Contests: Governmental and Populist Politics of Emotion'. The data gathering and cleaning processes are explained in detail in the Supplementary Information document present in this dataverse. This dataset contains the Tweet IDs of approximately 10.6 million tweets related to the 2016 Brexit Referendum in the UK (~0.87 million tweets) and 2016 United States presidential election of Donald Trump (~9.8 million tweets). They were collected between Jan 1, 2015 and Dec 31, 2020 from the Twitter API for Researchers. These Tweet IDs are presented into 11 collections. Each collection was collected from the GET statuses/user_timeline method of the Twitter REST API, following Twitter's Terms and Conditions. The collections are: Brexit Leave Political Actors: brexit_leave_pol_ids.txt Brexit Remain Political Actors: brexit_remain_pol_ids.txt Brexit News Media: brexit_media_ids.txt Brexit Society: brexit_soc_ids.txt US Democratic Political Actors: us_dem_pol_ids.txt US Republican Political Actors: us_rep_pol_ids.txt US News Media: us_media_ids.txt US Society (Elections): us_public_ele_ids.txt US Society (Biden): us_public_biden_ids.txt US Society (Clinton): us_public_clinton_ids.txt US Society (Trump): us_public_trump_ids.txt A README.txt file is also provided. The GET statuses/lookup method allows retrieving the complete Tweet (as well as full metadata: date, text, number of likes, retweets, etc.) for a Tweet ID. When retrieving information be aware that: Twitter may limit such a data retrieval. Please consult Twitter documentation. The Twitter API will not return tweets that have been deleted or belong to accounts that have been suspended, deleted, or made private. Therefore, several tweets may be unavailable. According to Twitter's documentation, duplicate tweets may appear when using filter streaming tools. Our dataset does not, however, contain duplicates. Following Twitter’s Developer Policy, Tweet IDs may be publicly shared; tweets' text and other information may not. Questions about this dataset can be sent to the authors.

Authoritarianism has resurfaced as a research topic in political psychology, as it appears relevant to explain current political trends. Authoritarian attitudes have been consistently linked to feelings of disgust, an emotion that is thought to have evolved to protect the organism from contamination. We hypothesized that body odour disgust sensitivity (BODS) might be associated with authoritarianism, as chemo-signalling is a primitive system for regulating interpersonal contact and disease avoidance, which are key features also in authoritarianism. We used well-validated scales for measuring BODS, authoritarianism and related constructs. Across two studies, we found that BODS is positively related to authoritarianism. In a third study, we showed a positive association between BODS scores and support for Donald Trump, who, at the time of data collection, was a presidential candidate with an agenda described as resonating with authoritarian attitudes. Authoritarianism fully explained the positive association between BODS and support for Donald Trump. Our findings highlight body odour disgust as a new and promising domain in political psychology research. Authoritarianism and BODS might be part of the same disease avoidance framework, and our results contribute to the growing evidence that contemporary social attitudes might be rooted in basic sensory functions.

According to Cognitive Market Research, the global Assembly Fastening Tools market size will be USD 3,514.2 million in 2025. It will expand at a compound annual growth rate (CAGR) of 5.00% from 2025 to 2033.

North America held the major market share for more than 37% of the global revenue with a market size of USD 1300.25 million in 2025 and will grow at a compound annual growth rate (CAGR) of 3.5% from 2025 to 2033.
Europe accounted for a market share of over 29% of the global revenue with a market size of USD 1019.12 million.
APAC held a market share of around 24% of the global revenue with a market size of USD 843.41 million in 2025 and will grow at a compound annual growth rate (CAGR) of 7.7% from 2025 to 2033.
South America has a market share of more than 4% of the global revenue with a market size of USD 133.54 million in 2025 and will grow at a compound annual growth rate (CAGR) of 6.3% from 2025 to 2033.
Middle East had a market share of around 4% of the global revenue and was estimated at a market size of USD 140.57 million in 2025 and will grow at a compound annual growth rate (CAGR) of 6.3% from 2025 to 2033.
Africa had a market share of around 2.2% of the global revenue and was estimated at a market size of USD 77.31 million in 2025 and will grow at a compound annual growth rate (CAGR) of 5.3% from 2025 to 2033.
Inline Tool is the fastest growing segment of the Assembly Fastening Tools industry

Market Dynamics of Assembly Fastening Tools Market

Key Drivers for Assembly Fastening Tools Market

Rising Demand from Automotive Industry Is Expected To Boost Market Growth

Automation is transforming automotive manufacturing, with robotic and smart fastening tools becoming integral to production lines. Automotive manufacturers are increasingly adopting torque-controlled and data-driven fastening systems to improve efficiency, reduce errors, and ensure consistency in mass production. The integration of fastening tools with Industry 4.0 technologies, such as IoT and AI-based monitoring, further enhances productivity by enabling real-time data collection and predictive maintenance. This technological advancement is crucial in maintaining the high production volumes required to meet the growing global demand for vehicles. Additionally, stringent safety and quality regulations in the automotive sector necessitate the use of high-performance fastening tools. Manufacturers must comply with international safety standards such as ISO, ensuring that fastened joints remain secure under extreme conditions, including vibrations and high-impact forces. As automakers continue to innovate with new vehicle designs, including lightweight structures and modular assembly techniques, the need for specialized fastening tools will continue to grow. In February 2025, Mercedes-Benz CEO Ola Källenius voiced concerns over President Trump's proposed 25% tariffs on the automotive industry, highlighting the company's substantial investments in the U.S. and the potential negative impact on both American and foreign car manufacturers.

https://www.the-sun.com/motors/13587592/mercedes-benz-ceo-fines-us-plans-trump-tariffs/

Growth in Aerospace and Defense Sector To Boost Market Growth

The aerospace and defense industry is one of the most demanding sectors when it comes to manufacturing precision, safety, and quality standards. Aircraft and defense equipment must meet strict regulatory requirements, necessitating the use of high-performance fastening tools for assembly and maintenance. Every component, from airframes and engines to avionics and interior structures, requires precise fastening solutions to ensure structural integrity, reliability, and resistance to extreme conditions such as high pressure, temperature fluctuations, and vibrations. This growing emphasis on quality and safety is driving the demand for advanced fastening tools designed specifically for aerospace and defense applications. In August 2023, the Pentagon's Space Development Agency awarded contracts totaling $1.5 billion to Lockheed Martin and Northrop Grumman for the development of 72 prototype communications satellites. These satellites are designed to provide encrypted communications for the U.S. military, forming part of the Proliferated Warfighter Space Architecture.

Restraint Factor for the Assembly Fastening Tools Market

High Initial Cost of Advanced Fastening Tools, Will Limit Market Growth

The ...

*** Fake News on Twitter ***

These 5 datasets are the results of an empirical study on the spreading process of newly fake news on Twitter. Particularly, we have focused on those fake news which have given rise to a truth spreading simultaneously against them. The story of each fake news is as follow:

1- FN1: A Muslim waitress refused to seat a church group at a restaurant, claiming "religious freedom" allowed her to do so.

2- FN2: Actor Denzel Washington said electing President Trump saved the U.S. from becoming an "Orwellian police state."

3- FN3: Joy Behar of "The View" sent a crass tweet about a fatal fire in Trump Tower.

4- FN4: The animated children's program 'VeggieTales' introduced a cannabis character in August 2018.

5- FN5: In September 2018, the University of Alabama football program ended its uniform contract with Nike, in response to Nike's endorsement deal with Colin Kaepernick.

The data collection has been done in two stages that each provided a new dataset: 1- attaining Dataset of Diffusion (DD) that includes information of fake news/truth tweets and retweets 2- Query of neighbors for spreaders of tweets that provides us with Dataset of Graph (DG).

DD

DD for each fake news story is an excel file, named FNx_DD where x is the number of fake news, and has the following structure:

The structure of excel files for each dataset is as follow:

Each row belongs to one captured tweet/retweet related to the rumor, and each column of the dataset presents a specific information about the tweet/retweet. These columns from left to right present the following information about the tweet/retweet:

User ID (user who has posted the current tweet/retweet)

The description sentence in the profile of the user who has published the tweet/retweet

The number of published tweet/retweet by the user at the time of posting the current tweet/retweet

Date and time of creation of the account by which the current tweet/retweet has been posted

Language of the tweet/retweet

Number of followers

Number of followings (friends)

Date and time of posting the current tweet/retweet

Number of like (favorite) the current tweet had been acquired before crawling it

Number of times the current tweet had been retweeted before crawling it

Is there any other tweet inside of the current tweet/retweet (for example this happens when the current tweet is a quote or reply or retweet)

The source (OS) of device by which the current tweet/retweet was posted

Tweet/Retweet ID

Retweet ID (if the post is a retweet then this feature gives the ID of the tweet that is retweeted by the current post)

Quote ID (if the post is a quote then this feature gives the ID of the tweet that is quoted by the current post)

Reply ID (if the post is a reply then this feature gives the ID of the tweet that is replied by the current post)

Frequency of tweet occurrences which means the number of times the current tweet is repeated in the dataset (for example the number of times that a tweet exists in the dataset in the form of retweet posted by others)

State of the tweet which can be one of the following forms (achieved by an agreement between the annotators):

r : The tweet/retweet is a fake news post

a : The tweet/retweet is a truth post

q : The tweet/retweet is a question about the fake news, however neither confirm nor deny it

n : The tweet/retweet is not related to the fake news (even though it contains the queries related to the rumor, but does not refer to the given fake news)

DG

DG for each fake news contains two files:

A file in graph format (.graph) which includes the information of graph such as who is linked to whom. (This file named FNx_DG.graph, where x is the number of fake news)

A file in Jsonl format (.jsonl) which includes the real user IDs of nodes in the graph file. (This file named FNx_Labels.jsonl, where x is the number of fake news)

Because in the graph file, the label of each node is the number of its entrance in the graph. For example if node with user ID 12345637 be the first node which has been entered into the graph file then its label in the graph is 0 and its real ID (12345637) would be at the row number 1 (because the row number 0 belongs to column labels) in the jsonl file and so on other node IDs would be at the next rows of the file (each row corresponds to 1 user id). Therefore, if we want to know for example what the user id of node 200 (labeled 200 in the graph) is, then in jsonl file we should look at row number 202.

The user IDs of spreaders in DG (those who have had a post in DD) would be available in DD to get extra information about them and their tweet/retweet. The other user IDs in DG are the neighbors of these spreaders and might not exist in DD.